You are here

West, Mike

Overview:

Go to https://stat.duke.edu/~mw/">my personal web page for links and info on my teaching, publication list (sortable and searchable -- just click on table headers), current research, current & past students, software, etc.

Positions:

Arts and Sciences Professor of Statistics and Decision Sciences

Statistical Science
Trinity College of Arts & Sciences

Professor of Statistical Science

Statistical Science
Trinity College of Arts & Sciences

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

News:

Awards:

American Statistical Association, NC Chapter Award. American Statistical Association.

Type
National
Awarded By
American Statistical Association
Date
January 01, 2014

Zellner Medal. Zellner Medal, International Society for Bayesian Analysis (ISBA).

Type
International
Awarded By
Zellner Medal, International Society for Bayesian Analysis (ISBA)
Date
January 01, 2014

Mitchell Prize. International Society for Bayesian Analysis, & Bayesian Statistical Science Section of the American Statistical Association.

Type
International
Awarded By
International Society for Bayesian Analysis, & Bayesian Statistical Science Section of the American Statistical Association
Date
January 01, 2012

Chair, Section on Bayesian Statistical Science. American Statistical Association.

Type
National
Awarded By
American Statistical Association
Date
January 01, 2010

President. International Society for Bayesian Analysis.

Type
International
Awarded By
International Society for Bayesian Analysis
Date
January 01, 2009

Best Papers of Annals of Applied Statistics 2007 Award. Annals of Applied Statistics.

Type
National
Awarded By
Annals of Applied Statistics
Date
August 01, 2008

Outstanding Statistical Application. American Statistical Association.

Type
National
Awarded By
American Statistical Association
Date
January 01, 1999

ASA Fellows. American Statistical Association.

Type
National
Awarded By
American Statistical Association
Date
January 01, 1993

Publications:

Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models

Authors
Gruber, LF; West, M
MLA Citation
Gruber, LF, and West, M. "Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models." Econometrics and Statistics 3 (July 2017): 3-22.
Source
crossref
Volume
3
Publish Date
2017
Start Page
3
End Page
22
DOI
10.1016/j.ecosta.2017.03.003

Dynamic dependence networks: Financial time series forecasting and portfolio decisions

Authors
Zhao, ZY; Xie, M; West, M
MLA Citation
Zhao, ZY, Xie, M, and West, M. "Dynamic dependence networks: Financial time series forecasting and portfolio decisions." Ed. H Yang. Applied Stochastic Models in Business and Industry 32.3 (May 2016): 311-332.
Source
crossref
Published In
Applied Stochastic Models in Business and Industry
Volume
32
Issue
3
Publish Date
2016
Start Page
311
End Page
332
DOI
10.1002/asmb.2161

Rejoinder to ‘Dynamic dependence networks: Financial time series forecasting and portfolio decisions’

Authors
Zhao, Z; Xie, M; West, M
MLA Citation
Zhao, Z, Xie, M, and West, M. "Rejoinder to ‘Dynamic dependence networks: Financial time series forecasting and portfolio decisions’." Ed. H Yang. Applied Stochastic Models in Business and Industry 32.3 (May 2016): 336-339.
Source
crossref
Published In
Applied Stochastic Models in Business and Industry
Volume
32
Issue
3
Publish Date
2016
Start Page
336
End Page
339
DOI
10.1002/asmb.2169

Models of random sparse eigenmatrices and Bayesian analysis of multivariate structure

© Springer International Publishing 2016. We discuss probabilistic models of random covariance structures defined by distributions over sparse eigenmatrices. The decomposition of orthogonal matrices in terms of Givens rotations defines a natural, interpretable framework for defining distributions on sparsity structure of random eigenmatrices. We explore theoretical aspects and implications for conditional independence structures arising in multivariate Gaussian models, and discuss connections with sparse PCA, factor analysis and Gaussian graphical models. Methodology includes model-based exploratory data analysis and Bayesian analysis via reversible jump Markov chain Monte Carlo. A simulation study examines the ability to identify sparse multivariate structures compared to the benchmark graphical modelling approach. Extensions to multivariate normal mixture models with additional measurement errors move into the framework of latent structure analysis of broad practical interest. We explore the implications and utility of the new models with summaries of a detailed applied study of a 20-dimensional breast cancer genomics data set.

Authors
Cron, A; West, M
MLA Citation
Cron, A, and West, M. "Models of random sparse eigenmatrices and Bayesian analysis of multivariate structure." January 1, 2016.
Source
scopus
Volume
11
Publish Date
2016
Start Page
125
End Page
153
DOI
10.1007/978-3-319-27099-9_7

Discriminative variable subsets in Bayesian classification with mixture models, with application in flow cytometry studies.

We discuss the evaluation of subsets of variables for the discriminative evidence they provide in multivariate mixture modeling for classification. The novel development of Bayesian classification analysis presented is partly motivated by problems of design and selection of variables in biomolecular studies, particularly involving widely used assays of large-scale single-cell data generated using flow cytometry technology. For such studies and for mixture modeling generally, we define discriminative analysis that overlays fitted mixture models using a natural measure of concordance between mixture component densities, and define an effective and computationally feasible method for assessing and prioritizing subsets of variables according to their roles in discrimination of one or more mixture components. We relate the new discriminative information measures to Bayesian classification probabilities and error rates, and exemplify their use in Bayesian analysis of Dirichlet process mixture models fitted via Markov chain Monte Carlo methods as well as using a novel Bayesian expectation-maximization algorithm. We present a series of theoretical and simulated data examples to fix concepts and exhibit the utility of the approach, and compare with prior approaches. We demonstrate application in the context of automatic classification and discriminative variable selection in high-throughput systems biology using large flow cytometry datasets.

Authors
Lin, L; Chan, C; West, M
MLA Citation
Lin, L, Chan, C, and West, M. "Discriminative variable subsets in Bayesian classification with mixture models, with application in flow cytometry studies." Biostatistics (Oxford, England) 17.1 (January 2016): 40-53.
PMID
26040910
Source
epmc
Published In
Biostatistics
Volume
17
Issue
1
Publish Date
2016
Start Page
40
End Page
53
DOI
10.1093/biostatistics/kxv021

GPU-accelerated Bayesian learning in simultaneous graphical dynamic linear models

Authors
Gruber, LF; West, M
MLA Citation
Gruber, LF, and West, M. "GPU-accelerated Bayesian learning in simultaneous graphical dynamic linear models." Bayesian Analysis 11 (2016): 125-149.
Source
manual
Published In
Bayesian Analysis
Volume
11
Publish Date
2016
Start Page
125
End Page
149
DOI
10.1214/15-BA946

Dynamic network signal processing using latent threshold models

Authors
Nakajima, J; West, M
MLA Citation
Nakajima, J, and West, M. "Dynamic network signal processing using latent threshold models." Digital Signal Processing 47 (December 2015): 5-16.
Source
crossref
Published In
Digital Signal Processing
Volume
47
Publish Date
2015
Start Page
5
End Page
16
DOI
10.1016/j.dsp.2015.04.008

Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation

Authors
Bonassi, FV; West, M
MLA Citation
Bonassi, FV, and West, M. Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation. March 2015.
Source
crossref
Published In
Bayesian Analysis
Publish Date
2015
Start Page
171
End Page
187
DOI
10.1214/14-BA891

Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models

Authors
Zhou, X; Nakajima, J; West, M
MLA Citation
Zhou, X, Nakajima, J, and West, M. "Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models." International Journal of Forecasting 30.4 (October 2014): 963-980.
Source
crossref
Published In
International Journal of Forecasting
Volume
30
Issue
4
Publish Date
2014
Start Page
963
End Page
980
DOI
10.1016/j.ijforecast.2014.03.017

CFEnetwork: The Annals of Computational and Financial Econometrics

Authors
Kontoghiorghes, EJ; Van Dijk, HK; Belsley, DA; Bollerslev, T; Diebold, FX; Dufour, J-M; Engle, R; Harvey, A; Koopman, SJ; Pesaran, H; Phillips, PCB; Smith, RJ; West, M; Yao, Q; Amendola, A; Billio, M; Chen, CWS; Chiarella, C; Colubi, A; Deistler, M; Francq, C; Hallin, M; Jacquier, E; Judd, K; Koop, G; Lütkepohl, H; MacKinnon, JG; Mittnik, S; Omori, Y; Pollock, DSG; Proietti, T; Rombouts, JVK; Scaillet, O; Semmler, W; So, MKP; Steel, M; Taylor, R; Tzavalis, E; Zakoian, J-M; Boswijk, HP; Luati, A et al.
MLA Citation
Kontoghiorghes, EJ, Van Dijk, HK, Belsley, DA, Bollerslev, T, Diebold, FX, Dufour, J-M, Engle, R, Harvey, A, Koopman, SJ, Pesaran, H, Phillips, PCB, Smith, RJ, West, M, Yao, Q, Amendola, A, Billio, M, Chen, CWS, Chiarella, C, Colubi, A, Deistler, M, Francq, C, Hallin, M, Jacquier, E, Judd, K, Koop, G, Lütkepohl, H, MacKinnon, JG, Mittnik, S, Omori, Y, Pollock, DSG, Proietti, T, Rombouts, JVK, Scaillet, O, Semmler, W, So, MKP, Steel, M, Taylor, R, Tzavalis, E, Zakoian, J-M, Boswijk, HP, and Luati, A et al. "CFEnetwork: The Annals of Computational and Financial Econometrics." Computational Statistics & Data Analysis 76 (August 2014): 1-3.
Source
crossref
Published In
Computational Statistics & Data Analysis
Volume
76
Publish Date
2014
Start Page
1
End Page
3
DOI
10.1016/j.csda.2014.04.006

Bayesian computation for immune response modeling with sparse time series data

Authors
Bonassi, FV; Chan, C; West, M
MLA Citation
Bonassi, FV, Chan, C, and West, M. Bayesian computation for immune response modeling with sparse time series data. Department of Statistical Science, Duke University, May 15, 2014.
Source
manual
Publish Date
2014

Bayesian forecasting and portfolio decisions using dynamic dependent factor models

Authors
Zhou, X; Nakajima, J; West, M
MLA Citation
Zhou, X, Nakajima, J, and West, M. "Bayesian forecasting and portfolio decisions using dynamic dependent factor models." International Journal of Forecasting - (2014): ---. (Academic Article)
Source
manual
Published In
International Journal of Forecasting
Volume
-
Publish Date
2014
Start Page
-
End Page
-

CFEnetwork: The Annals of Computational and Financial Econometrics: 2nd Issue.

Authors
Kontoghiorghes, EJ; Dijk, HKV; Belsley, DA; Bollerslev, T; Diebold, FX; Dufour, J-M; Engle, R; Harvey, A; Koopman, SJ; Pesaran, H; Phillips, PCB; Smith, RJ; West, M; Yao, Q; Amendola, A; Billio, M; Chen, CWS; Chiarella, C; Colubi, A; Deistler, M; Francq, C; Hallin, M; Jacquier, E; Judd, K; Koop, G; Lütkepohl, H; MacKinnon, JG; Mittnik, S; Omori, Y; Pollock, DSG; Proietti, T; Rombouts, JVK; Scaillet, O; Semmler, W; So, MKP; Steel, M; Taylor, R; Tzavalis, E; Zakoian, J-M; Boswijk, HP; Luati, A et al.
MLA Citation
Kontoghiorghes, EJ, Dijk, HKV, Belsley, DA, Bollerslev, T, Diebold, FX, Dufour, J-M, Engle, R, Harvey, A, Koopman, SJ, Pesaran, H, Phillips, PCB, Smith, RJ, West, M, Yao, Q, Amendola, A, Billio, M, Chen, CWS, Chiarella, C, Colubi, A, Deistler, M, Francq, C, Hallin, M, Jacquier, E, Judd, K, Koop, G, Lütkepohl, H, MacKinnon, JG, Mittnik, S, Omori, Y, Pollock, DSG, Proietti, T, Rombouts, JVK, Scaillet, O, Semmler, W, So, MKP, Steel, M, Taylor, R, Tzavalis, E, Zakoian, J-M, Boswijk, HP, and Luati, A et al. "CFEnetwork: The Annals of Computational and Financial Econometrics: 2nd Issue." Computational Statistics & Data Analysis 76 (2014): 1-3.
Source
dblp
Published In
Computational Statistics & Data Analysis
Volume
76
Publish Date
2014
Start Page
1
End Page
3
DOI
10.1016/j.csda.2014.04.006

Spatially-varying SAR models and Bayesian inference for high-resolution lattice data

Authors
Mukherjee, C; Kasibhatla, PS; West, M
MLA Citation
Mukherjee, C, Kasibhatla, PS, and West, M. "Spatially-varying SAR models and Bayesian inference for high-resolution lattice data." Annals of the Institute of Statistical Mathematics 66 (2014): 000-000. (Academic Article)
Source
manual
Published In
Annals of the Institute of Statistical Mathematics
Volume
66
Publish Date
2014
Start Page
000
End Page
000
DOI
10.1007/s10463-013-0426-9

Sparse and dynamic transfer response factor models via Bayesian latent thresholding

Authors
Nakajima, J; West, M
MLA Citation
Nakajima, J, and West, M. Sparse and dynamic transfer response factor models via Bayesian latent thresholding. Department of Statistical Science, Duke University, June 2013.
Source
manual
Publish Date
2013

Autoregressive models for variance matrices: Stationary inverse Wishart processes

Authors
Fox, EB; West, M
MLA Citation
Fox, EB, and West, M. Autoregressive models for variance matrices: Stationary inverse Wishart processes. Department of Statistical Science, Duke University, 2013.
Source
manual
Publish Date
2013

Discriminative variable subsets in Bayesian classification with mixture models

Authors
Lin, L; Chan, C; West, M
MLA Citation
Lin, L, Chan, C, and West, M. Discriminative variable subsets in Bayesian classification with mixture models. Department of Statistical Science, Duke University, 2013.
Source
manual
Publish Date
2013

Dynamic discounting for model mixing in Bayesian forecasting

Authors
Xie, M; West, M
MLA Citation
Xie, M, and West, M. Dynamic discounting for model mixing in Bayesian forecasting. Department of Statistical Science, Duke University, 2013.
Source
manual
Publish Date
2013

Bayesian dynamic modelling

Authors
West, M
MLA Citation
West, M. "Bayesian dynamic modelling." Bayesian Theory and Applications. Ed. P Damien, P Dellaportes, NG Polson, and DA Stephens. Clarendon: Oxford University Press, 2013. 145-166.
Source
manual
Publish Date
2013
Start Page
145
End Page
166

The Oxford Handbook of Applied Bayesian Analysis

Bayesian analysis has developed rapidly in applications in the last two decades and research in Bayesian methods remains dynamic and fast-growing. Dramatic advances in modelling concepts and computational technologies now enable routine application of Bayesian analysis using increasingly realistic stochastic models, and this drives the adoption of Bayesian approaches in many areas of science, technology, commerce, and industry. This Handbook explores contemporary Bayesian analysis across a variety of application areas. Chapters written by leading exponents of applied Bayesian analysis showcase the scientific ease and natural application of Bayesian modelling, and present solutions to real, engaging, societally important and demanding problems. The chapters are grouped into five general areas: Biomedical & Health Sciences; Industry, Economics & Finance; Environment & Ecology; Policy, Political & Social Sciences; and Natural & Engineering Sciences, and Appendix material in each touches on key concepts, models, and techniques of the chapter that are also of broader pedagogic and applied interest. Contributors to this volume - David Dunson Peter Green, Kanti Mardia, Vysaul Nyirongo & Yann Ruffieux Jerry Cheng & David Madigan Jeremy Oakley & Helen Clough Alexandra Schmidt, Jennifer Hoeting, Joao Batista Pereira & Pedro Paulo Vieira Dan Merl, Joseph Lucas, Joseph Nevins, Haige Shenz & Mike West D. A. Henderson, R.J. Boys, C.J. Proctor & D.J. Wilkinson Elmira Popova, David Morton, Paul Damien & Tim Hanson Jonathan Cumming & Michael Goldstein Antonio Pievatolo & Fabrizio Ruggeri Marco Ferreira, Adelmo Bertoldey & Scott Holan Hedibert Lopes & Nicholas Polson Jose Mario Quintana, Carlos Carvalho, James Scott & Thomas Costigliola Jesus Fernandez-Villaverde, Pablo Guerron-Quintana & Juan Rubio-Ramirez Peter Challenor, Doug McNeall & James Gattiker James Clark, Dave Bell, Michael Dietze, Michelle Hersh, Ines Ibanez, Shannon LaDeau, Sean McMahon, Jessica Metcalf, Emily Moran, Luke Pangle & Mike Wolosin Alan Gelfand & Sujit K. Sahu Samantha Low Choy, Justine Murray, Allan James & Kerrie Mengersen

MLA Citation
The Oxford Handbook of Applied Bayesian Analysis. Ed. A O' Hagan and M West. 2013.
Source
repec
Publish Date
2013

Bayesian analysis of latent threshold dynamic models

Authors
Nakajima, J; West, M
MLA Citation
Nakajima, J, and West, M. "Bayesian analysis of latent threshold dynamic models." Journal of Business & Economic Statistics 31 (2013): 151-164. (Academic Article)
Website
http://hdl.handle.net/10161/6152
Source
manual
Published In
Journal of Business & Economic Statistics
Volume
31
Publish Date
2013
Start Page
151
End Page
164
DOI
10.1080/07350015.2012.747847

Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.

Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less). Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM) approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM) naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC) samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a consistent labeling of cell subsets and increase the sensitivity of rare event detection in the context of quantifying antigen-specific immune responses.

Authors
Cron, A; Gouttefangeas, C; Frelinger, J; Lin, L; Singh, SK; Britten, CM; Welters, MJP; van der Burg, SH; West, M; Chan, C
MLA Citation
Cron, A, Gouttefangeas, C, Frelinger, J, Lin, L, Singh, SK, Britten, CM, Welters, MJP, van der Burg, SH, West, M, and Chan, C. "Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples." PLoS Comput Biol 9.7 (2013): e1003130-.
PMID
23874174
Source
pubmed
Published In
PLoS computational biology
Volume
9
Issue
7
Publish Date
2013
Start Page
e1003130
DOI
10.1371/journal.pcbi.1003130

Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies

Authors
Lin, L; Chan, C; Hadrup, SR; Froesig, TM; Wang, Q; West, M
MLA Citation
Lin, L, Chan, C, Hadrup, SR, Froesig, TM, Wang, Q, and West, M. "Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies." STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY 12.3 (2013): 309-331.
PMID
23629459
Source
wos-lite
Published In
Statistical Applications in Genetics and Molecular Biology
Volume
12
Issue
3
Publish Date
2013
Start Page
309
End Page
331
DOI
10.1515/sagmb-2012-0001

Model-controlled flooding with applications to image reconstruction and segmentation.

We discuss improved image reconstruction and segmentation in a framework we term model-controlled flooding (MCF). This extends the watershed transform for segmentation by allowing the integration of a priori information about image objects into flooding simulation processes. Modeling the initial seeding, region growing, and stopping rules of the watershed flooding process allows users to customize the simulation with user-defined or default model functions incorporating prior information. It also extends a more general class of transforms based on connected attribute filters by allowing the modification of connected components of a grayscale image, thus providing more flexibility in image reconstruction. MCF reconstruction defines images with desirable features for further segmentation using existing methods and can lead to substantial improvements. We demonstrate the MCF framework using a size transform that extends grayscale area opening and attribute thickening/thinning, and give examples from several areas: concealed object detection, speckle counting in biological single cell studies, and analyses of benchmark microscopic image data sets. MCF achieves benchmark error rates well below those reported in the recent literature and in comparison with other algorithms, while being easily adapted to new imaging contexts.

Authors
Wang, Q; West, M
MLA Citation
Wang, Q, and West, M. "Model-controlled flooding with applications to image reconstruction and segmentation." J Electron Imaging 21.2 (June 22, 2012).
PMID
23049229
Source
pubmed
Published In
Journal of Electronic Imaging
Volume
21
Issue
2
Publish Date
2012
DOI
10.1117/1.JEI.21.2.023020

Preface

Authors
Bernardo, JM; Bayarri, MJ; Berger, JO; Dawid, AP; Heckerman, D; Smith, AFM; West, M
MLA Citation
Bernardo, JM, Bayarri, MJ, Berger, JO, Dawid, AP, Heckerman, D, Smith, AFM, and West, M. "Preface." January 19, 2012.
Source
scopus
Publish Date
2012
DOI
10.1093/acprof:oso/9780199694587.002.0004

Bayesian Statistics 9

© Oxford University Press 2011. All rights reserved. The Valencia International Meetings on Bayesian Statistics - established in 1979 and held every four years - have been the forum for a definitive overview of current concerns and activities in Bayesian statistics. These are the edited Proceedings of the Ninth meeting, and contain the invited papers each followed by their discussion and a rejoinder by the author(s). In the tradition of the earlier editions, this encompasses an enormous range of theoretical and applied research, highlighting the breadth, vitality and impact of Bayesian thinking in interdisciplinary research across many fields as well as the corresponding growth and vitality of core theory and methodology. The Valencia 9 invited papers cover a broad range of topics, including foundational and core theoretical issues in statistics, the continued development of new and refined computational methods for complex Bayesian modelling, substantive applications of flexible Bayesian modelling, and new developments in the theory and methodology of graphical modelling. They also describe advances in methodology for specific applied fields, including financial econometrics and portfolio decision making, public policy applications for drug surveillance, studies in the physical and environmental sciences, astronomy and astrophysics, climate change studies, molecular biosciences, statistical genetics or stochastic dynamic networks in systems biology.

Authors
Bernardo, JM; Bayarri, MJ; Berger, JO; Dawid, AP; Heckerman, D; Smith, AFM; West, M
MLA Citation
Bernardo, JM, Bayarri, MJ, Berger, JO, Dawid, AP, Heckerman, D, Smith, AFM, and West, M. "Bayesian Statistics 9." January 19, 2012.
Source
scopus
Published In
Bayesian Statistics 9
Publish Date
2012
Start Page
1
End Page
720
DOI
10.1093/acprof:oso/9780199694587.001.0001

Models of random sparse eigenmatrices matrices with application to Bayesian factor analysis

Authors
Cron, AJ; West, M
MLA Citation
Cron, AJ, and West, M. Models of random sparse eigenmatrices matrices with application to Bayesian factor analysis. Department of Statistical Science, Duke University, 2012.
Source
manual
Publish Date
2012

Dynamic factor volatility modeling: A bayesian latent threshold approach

We discuss dynamic factor modeling of financial time series using a latent threshold approach to factor volatility. This approach models time-varying patterns of occurrence of zero elements in factor loadings matrices, providing adaptation to changing relationships over time and dynamic model selection. We summarize Bayesian methods for model fitting and discuss analyses of several FX, commodities, and stock price index time series. Empirical results show that the latent threshold approach can define interpretable, data-driven, dynamic sparsity, leading to reduced estimation uncertainties, improved predictions, and portfolio performance in increasingly high-dimensional dynamic factor models. © The Author, 2012. Published by Oxford University Press. All rights reserved.

Authors
Nakajima, J; West, M
MLA Citation
Nakajima, J, and West, M. "Dynamic factor volatility modeling: A bayesian latent threshold approach." Journal of Financial Econometrics 11.1 (2012): 116-153.
Source
scival
Published In
Journal of Financial Econometrics
Volume
11
Issue
1
Publish Date
2012
Start Page
116
End Page
153
DOI
10.1093/jjfinec/nbs013

Bayesian spatio-dynamic modeling in cell motility studies: Learning nonlinear taxic fields guiding the immune response

We develop and analyze models of the spatio-temporal organization of lymphocytes in the lymph nodes and spleen. The spatial dynamics of these immune system white blood cells are influenced by biochemical fields and represent key components of the overall immune response to vaccines and infections. A primary goal is to learn about the structure of these fields that fundamentally shape the immune response. We define dynamic models of single-cell motion involving nonparametric representations of scalar potential fields underlying the directional biochemical fields that guide cellular motion. Bayesian hierarchical extensions define multicellular models for aggregating models and data on colonies of cells. Analysis via customized Markov chain Monte Carlo methods leads to Bayesian inference on cell-specific and population parameters together with the underlying spatial fields. Our case study explores data from multiphoton intravital microscopy in lymph nodes of mice, and we use a number of visualization tools to summarize and compare posterior inferences on the three-dimensional taxic fields. © 2012 American Statistical Association.

Authors
Manolopoulou, I; Matheu, MP; Cahalan, MD; West, M; Kepler, TB
MLA Citation
Manolopoulou, I, Matheu, MP, Cahalan, MD, West, M, and Kepler, TB. "Bayesian spatio-dynamic modeling in cell motility studies: Learning nonlinear taxic fields guiding the immune response." Journal of the American Statistical Association 107.499 (2012): 855-865.
Source
scival
Published In
Journal of the American Statistical Association
Volume
107
Issue
499
Publish Date
2012
Start Page
855
End Page
865
DOI
10.1080/01621459.2012.655995

Rejoinder

Authors
Manolopoulou, I; Matheu, MP; Cahalan, MD; West, M; Kepler, TB
MLA Citation
Manolopoulou, I, Matheu, MP, Cahalan, MD, West, M, and Kepler, TB. "Rejoinder." Journal of the American Statistical Association 107.499 (2012): 871-874.
Source
scival
Published In
Journal of the American Statistical Association
Volume
107
Issue
499
Publish Date
2012
Start Page
871
End Page
874
DOI
10.1080/01621459.2012.714715

Bayesian learning from marginal data in bionetwork models.

In studies of dynamic molecular networks in systems biology, experiments are increasingly exploiting technologies such as flow cytometry to generate data on marginal distributions of a few network nodes at snapshots in time. For example, levels of intracellular expression of a few genes, or cell surface protein markers, can be assayed at a series of interim time points and assumed steady-states under experimentally stimulated growth conditions in small cellular systems. Such marginal data on a small number of cellular markers will typically carry very limited information on the parameters and structure of dynamic network models, though experiments will typically be designed to expose variation in cellular phenotypes that are inherently related to some aspects of model parametrization and structure. Our work addresses statistical questions of how to integrate such data with dynamic stochastic models in order to properly quantify the information-or lack of information-it carries relative to models assumed. We present a Bayesian computational strategy coupled with a novel approach to summarizing and numerically characterizing biological phenotypes that are represented in terms of the resulting sample distributions of cellular markers. We build on Bayesian simulation methods and mixture modeling to define the approach to linking mechanistic mathematical models of network dynamics to snapshot data, using a toggle switch example integrating simulated and real data as context.

Authors
Bonassi, FV; You, L; West, M
MLA Citation
Bonassi, FV, You, L, and West, M. "Bayesian learning from marginal data in bionetwork models. (Published online)" Stat Appl Genet Mol Biol 10.1 (October 27, 2011).
PMID
23089812
Source
pubmed
Published In
Statistical Applications in Genetics and Molecular Biology
Volume
10
Issue
1
Publish Date
2011
DOI
10.2202/1544-6115.1684

Computation of steady-state probability distributions in stochastic models of cellular networks.

Cellular processes are "noisy". In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry.

Authors
Hallen, M; Li, B; Tanouchi, Y; Tan, C; West, M; You, L
MLA Citation
Hallen, M, Li, B, Tanouchi, Y, Tan, C, West, M, and You, L. "Computation of steady-state probability distributions in stochastic models of cellular networks." PLoS Comput Biol 7.10 (October 2011): e1002209-.
PMID
22022252
Source
pubmed
Published In
PLoS computational biology
Volume
7
Issue
10
Publish Date
2011
Start Page
e1002209
DOI
10.1371/journal.pcbi.1002209

Efficient Classification-Based Relabeling in Mixture Models.

Effective component relabeling in Bayesian analyses of mixture models is critical to the routine use of mixtures in classification with analysis based on Markov chain Monte Carlo methods. The classification-based relabeling approach here is computationally attractive and statistically effective, and scales well with sample size and number of mixture components concordant with enabling routine analyses of increasingly large data sets. Building on the best of existing methods, practical relabeling aims to match data:component classification indicators in MCMC iterates with those of a defined reference mixture distribution. The method performs as well as or better than existing methods in small dimensional problems, while being practically superior in problems with larger data sets as the approach is scalable. We describe examples and computational benchmarks, and provide supporting code with efficient computational implementation of the algorithm that will be of use to others in practical applications of mixture models.

Authors
Cron, AJ; West, M
MLA Citation
Cron, AJ, and West, M. "Efficient Classification-Based Relabeling in Mixture Models." Am Stat 65.1 (February 1, 2011): 16-20.
PMID
21660126
Source
pubmed
Published In
The American statistician
Volume
65
Issue
1
Publish Date
2011
Start Page
16
End Page
20
DOI
10.1198/tast.2011.10170

Bayesian statistical modeling of spatially correlated error structure in atmospheric tracer inverse analysis

We present and discuss the use of Bayesian modeling and computational methods for atmospheric chemistry inverse analyses that incorporate evaluation of spatial structure in model-data residuals. Motivated by problems of refining bottom-up estimates of source/sink fluxes of trace gas and aerosols based on satellite retrievals of atmospheric chemical concentrations, we address the need for formal modeling of spatial residual error structure in global scale inversion models. We do this using analytically and computationally tractable conditional autoregressive (CAR) spatial models as components of a global inversion framework. We develop Markov chain Monte Carlo methods to explore and fit these spatial structures in an overall statistical framework that simultaneously estimates source fluxes. Additional aspects of the study extend the statistical framework to utilize priors on source fluxes in a physically realistic manner, and to formally address and deal with missing data in satellite retrievals. We demonstrate the analysis in the context of inferring carbon monoxide (CO) sources constrained by satellite retrievals of column CO from the Measurement of Pollution in the Troposphere (MOPITT) instrument on the TERRA satellite, paying special attention to evaluating performance of the inverse approach using various statistical diagnostic metrics. This is developed using synthetic data generated to resemble MOPITT data to define a proof-of-concept and model assessment, and then in analysis of real MOPITT data. These studies demonstrate the ability of these simple spatial models to substantially improve over standard non-spatial models in terms of statistical fit, ability to recover sources in synthetic examples, and predictive match with real data. © 2011 Author(s).

Authors
Mukherjee, C; Kasibhatla, PS; West, M
MLA Citation
Mukherjee, C, Kasibhatla, PS, and West, M. "Bayesian statistical modeling of spatially correlated error structure in atmospheric tracer inverse analysis." Atmospheric Chemistry and Physics 11.11 (2011): 5365-5382.
Source
scival
Published In
Atmospheric Chemistry and Physics
Volume
11
Issue
11
Publish Date
2011
Start Page
5365
End Page
5382
DOI
10.5194/acp-11-5365-2011

Origin of bistability underlying mammalian cell cycle entry

Precise control of cell proliferation is fundamental to tissue homeostasis and differentiation. Mammalian cells commit to proliferation at the restriction point (R-point). It has long been recognized that the R-point is tightly regulated by the Rb-E2F signaling pathway. Our recent work has further demonstrated that this regulation is mediated by a bistable switch mechanism. Nevertheless, the essential regulatory features in the Rb-E2F pathway that create this switching property have not been defined. Here we analyzed a library of gene circuits comprising all possible link combinations in a simplified Rb-E2F network. We identified a minimal circuit that is able to generate robust, resettable bistability. This minimal circuit contains a feed-forward loop coupled with a mutual-inhibition feedback loop, which forms an AND-gate control of the E2F activation. Underscoring its importance, experimental disruption of this circuit abolishes maintenance of the activated E2F state, supporting its importance for the bistability of the Rb-E2F system. Our findings suggested basic design principles for the robust control of the bistable cell cycle entry at the R-point. © 2011 EMBO and Macmillan Publishers Limited All rights reserved.

Authors
Yao, G; Tan, C; West, M; Nevins, JR; You, L
MLA Citation
Yao, G, Tan, C, West, M, Nevins, JR, and You, L. "Origin of bistability underlying mammalian cell cycle entry." Molecular Systems Biology 7 (2011).
PMID
21525871
Source
scival
Published In
Molecular systems biology
Volume
7
Publish Date
2011
DOI
10.1038/msb.2011.19

Optimization of a highly standardized carboxyfluorescein succinimidyl ester flow cytometry panel and gating strategy design using discriminative information measure evaluation.

The design of a panel to identify target cell subsets in flow cytometry can be difficult when specific markers unique to each cell subset do not exist, and a combination of parameters must be used to identify target cells of interest and exclude irrelevant events. Thus, the ability to objectively measure the contribution of a parameter or group of parameters toward target cell identification independent of any gating strategy could be very helpful for both panel design and gating strategy design. In this article, we propose a discriminative information measure evaluation (DIME) based on statistical mixture modeling; DIME is a numerical measure of the contribution of different parameters towards discriminating a target cell subset from all the others derived from the fitted posterior distribution of a Gaussian mixture model. Informally, DIME measures the "usefulness" of each parameter for identifying a target cell subset. We show how DIME provides an objective basis for inclusion or exclusion of specific parameters in a panel, and how ranked sets of such parameters can be used to optimize gating strategies. An illustrative example of the application of DIME to streamline the gating strategy for a highly standardized carboxyfluorescein succinimidyl ester (CFSE) assay is described.

Authors
Chan, C; Lin, L; Frelinger, J; Hérbert, V; Gagnon, D; Landry, C; Sékaly, R-P; Enzor, J; Staats, J; Weinhold, KJ; Jaimes, M; West, M
MLA Citation
Chan, C, Lin, L, Frelinger, J, Hérbert, V, Gagnon, D, Landry, C, Sékaly, R-P, Enzor, J, Staats, J, Weinhold, KJ, Jaimes, M, and West, M. "Optimization of a highly standardized carboxyfluorescein succinimidyl ester flow cytometry panel and gating strategy design using discriminative information measure evaluation." Cytometry A 77.12 (December 2010): 1126-1136.
PMID
21053294
Source
pubmed
Published In
Cytometry
Volume
77
Issue
12
Publish Date
2010
Start Page
1126
End Page
1136
DOI
10.1002/cyto.a.20987

Lactic acidosis triggers starvation response with paradoxical induction of TXNIP through MondoA.

Although lactic acidosis is a prominent feature of solid tumors, we still have limited understanding of the mechanisms by which lactic acidosis influences metabolic phenotypes of cancer cells. We compared global transcriptional responses of breast cancer cells in response to three distinct tumor microenvironmental stresses: lactic acidosis, glucose deprivation, and hypoxia. We found that lactic acidosis and glucose deprivation trigger highly similar transcriptional responses, each inducing features of starvation response. In contrast to their comparable effects on gene expression, lactic acidosis and glucose deprivation have opposing effects on glucose uptake. This divergence of metabolic responses in the context of highly similar transcriptional responses allows the identification of a small subset of genes that are regulated in opposite directions by these two conditions. Among these selected genes, TXNIP and its paralogue ARRDC4 are both induced under lactic acidosis and repressed with glucose deprivation. This induction of TXNIP under lactic acidosis is caused by the activation of the glucose-sensing helix-loop-helix transcriptional complex MondoA:Mlx, which is usually triggered upon glucose exposure. Therefore, the upregulation of TXNIP significantly contributes to inhibition of tumor glycolytic phenotypes under lactic acidosis. Expression levels of TXNIP and ARRDC4 in human cancers are also highly correlated with predicted lactic acidosis pathway activities and associated with favorable clinical outcomes. Lactic acidosis triggers features of starvation response while activating the glucose-sensing MondoA-TXNIP pathways and contributing to the "anti-Warburg" metabolic effects and anti-tumor properties of cancer cells. These results stem from integrative analysis of transcriptome and metabolic response data under various tumor microenvironmental stresses and open new paths to explore how these stresses influence phenotypic and metabolic adaptations in human cancers.

Authors
Chen, JL-Y; Merl, D; Peterson, CW; Wu, J; Liu, PY; Yin, H; Muoio, DM; Ayer, DE; West, M; Chi, J-T
MLA Citation
Chen, JL-Y, Merl, D, Peterson, CW, Wu, J, Liu, PY, Yin, H, Muoio, DM, Ayer, DE, West, M, and Chi, J-T. "Lactic acidosis triggers starvation response with paradoxical induction of TXNIP through MondoA. (Published online)" PLoS Genet 6.9 (September 2, 2010): e1001093-.
Website
http://hdl.handle.net/10161/4477
PMID
20844768
Source
pubmed
Published In
PLoS genetics
Volume
6
Issue
9
Publish Date
2010
Start Page
e1001093
DOI
10.1371/journal.pgen.1001093

Adaptive Mixture Modelling Metropolis Methods for Bayesian Analysis of Non-linear State-Space Models.

We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the non-linearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters. We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.

Authors
Niemi, J; West, M
MLA Citation
Niemi, J, and West, M. "Adaptive Mixture Modelling Metropolis Methods for Bayesian Analysis of Non-linear State-Space Models." J Comput Graph Stat 19.2 (June 1, 2010): 260-280.
Website
http://hdl.handle.net/10161/4403
PMID
20563281
Source
pubmed
Published In
Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
Volume
19
Issue
2
Publish Date
2010
Start Page
260
End Page
280

Time Series: Modeling, Computation, and Inference

Along with core models and methods, this text offers sophisticated tools for analyzing challenging time series problems. It also demonstrates the growth of time series analysis into new application areas.

Authors
Prado, R; West, M
MLA Citation
Prado, R, and West, M. Time Series: Modeling, Computation, and Inference. CRC Press, May 21, 2010.
Source
manual
Publish Date
2010

Trans-study projection of genomic biomarkers using sparse factor regression models

Authors
Merl, D; Lucas, JE; Nevins, JR; Shen, H; West, M
MLA Citation
Merl, D, Lucas, JE, Nevins, JR, Shen, H, and West, M. "Trans-study projection of genomic biomarkers using sparse factor regression models." The Handbook of Applied Bayesian Analysis. Ed. AO Hagan and M West. Oxford University Press, April 2010. 118-154.
Source
manual
Publish Date
2010
Start Page
118
End Page
154

Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy.

An increasingly common component of studies in synthetic and systems biology is analysis of dynamics of gene expression at the single-cell level, a context that is heavily dependent on the use of time-lapse movies. Extracting quantitative data on the single-cell temporal dynamics from such movies remains a major challenge. Here, we describe novel methods for automating key steps in the analysis of single-cell, fluorescent images-segmentation and lineage reconstruction-to recognize and track individual cells over time. The automated analysis iteratively combines a set of extended morphological methods for segmentation, and uses a neighborhood-based scoring method for frame-to-frame lineage linking. Our studies with bacteria, budding yeast and human cells, demonstrate the portability and usability of these methods, whether using phase, bright field or fluorescent images. These examples also demonstrate the utility of our integrated approach in facilitating analyses of engineered and natural cellular networks in diverse settings. The automated methods are implemented in freely available, open-source software.

Authors
Wang, Q; Niemi, J; Tan, C-M; You, L; West, M
MLA Citation
Wang, Q, Niemi, J, Tan, C-M, You, L, and West, M. "Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy." Cytometry A 77.1 (January 2010): 101-110.
PMID
19845017
Source
pubmed
Published In
Cytometry
Volume
77
Issue
1
Publish Date
2010
Start Page
101
End Page
110
DOI
10.1002/cyto.a.20812

Some of the What?, Why?, How?, Who? and Where? of graphics processing unit computing for Bayesian analysis

Authors
Suchard, MA; Holmes, C; West, M
MLA Citation
Suchard, MA, Holmes, C, and West, M. "Some of the What?, Why?, How?, Who? and Where? of graphics processing unit computing for Bayesian analysis." Bulletin of the International Society for Bayesian Analysis 17 (2010): 12-16. (Academic Article)
Source
manual
Published In
Bulletin of the International Society for Bayesian Analysis
Volume
17
Publish Date
2010
Start Page
12
End Page
16

Bounded approximations for marginal likelihoods

Authors
Ji, C; Shen, H; West, M
MLA Citation
Ji, C, Shen, H, and West, M. Bounded approximations for marginal likelihoods. Department of Statistical Science, Duke University, 2010.
Source
manual
Publish Date
2010

In-vitro to In-vivo factor profiling in expression genomics

Authors
Lucas, JE; Carvalho, CM; Merl, D; West, M
MLA Citation
Lucas, JE, Carvalho, CM, Merl, D, and West, M. "In-vitro to In-vivo factor profiling in expression genomics." Bayesian Modelling in Bioinformatics. Ed. D Dey, S Ghosh, and B Mallick. Taylor-Francis, 2010. p293-316.
Source
manual
Publish Date
2010
Start Page
p293
End Page
316

Statistical analysis of immunofluorescent histology

Authors
Manolopoulou, I; Wang, X; Ji, C; Lynch, HE; Stewart, S; Sempowski, GD; Alam, SM; West, M; Kepler, TB
MLA Citation
Manolopoulou, I, Wang, X, Ji, C, Lynch, HE, Stewart, S, Sempowski, GD, Alam, SM, West, M, and Kepler, TB. Statistical analysis of immunofluorescent histology. Department of Statistical Science, Duke University, 2010.
Source
manual
Publish Date
2010

Time Series: Modelling, Computation & Inference

Authors
Prado, R; West, M
MLA Citation
Prado, R, and West, M. Time Series: Modelling, Computation & Inference. Chapman & Hall/CRC Press, 2010.
Source
manual
Publish Date
2010

Bayesian modelling for biological pathway annotation of gene expression pathway signatures

Authors
Shen, H; West, M
MLA Citation
Shen, H, and West, M. "Bayesian modelling for biological pathway annotation of gene expression pathway signatures." Frontiers of Statistical Decision Making and Bayesian Analysis. Ed. M-H Chen, DK Dey, P Mueller, D Sun, and K Ye. New York: Springer-Verlag, 2010. 285-302.
Source
manual
Publish Date
2010
Start Page
285
End Page
302

Bayesian forecasting

Authors
West, M
MLA Citation
West, M. "Bayesian forecasting." Methods and Applications of Statistics in Business, Finance and Management Science. Ed. N Balakrishnan. Wiley, 2010. p72-84.
Source
manual
Publish Date
2010
Start Page
p72
End Page
84

Image segmentation and dynamic lineage analysis in single-cell fluorescent microscopy

Authors
Wang, Q; Niemi, JB; Tan, CM; You, L; West, M
MLA Citation
Wang, Q, Niemi, JB, Tan, CM, You, L, and West, M. "Image segmentation and dynamic lineage analysis in single-cell fluorescent microscopy." Cytometry A 77 (2010): 101-110. (Academic Article)
Source
manual
Published In
Cytometry A
Volume
77
Publish Date
2010
Start Page
101
End Page
110
DOI
10.1002/cyto.a.20812

Bayesian learning in sparse graphical factor models via annealed entropy

Authors
Yoshida, R; West, M
MLA Citation
Yoshida, R, and West, M. "Bayesian learning in sparse graphical factor models via annealed entropy." Journal of Machine Learning Research 11 (2010): 1771-1798. (Academic Article)
Source
manual
Published In
Journal of Machine Learning Research
Volume
11
Publish Date
2010
Start Page
1771
End Page
1798

Selection sampling from large data sets for targeted inference in mixture modeling (with discussion)

Authors
Manolopoulou, I; Chan, C; West, M
MLA Citation
Manolopoulou, I, Chan, C, and West, M. "Selection sampling from large data sets for targeted inference in mixture modeling (with discussion)." Bayesian Analysis 5 (2010): 429-450. (Academic Article)
Source
manual
Published In
Bayesian Analysis
Volume
5
Publish Date
2010
Start Page
429
End Page
450
DOI
10.1214/10-BA51

Understanding GPU programming for statistical computation: Studies in massively parallel massive mixtures

This article describes advances in statistical computation for large-scale data analysis in structured Bayesian mixture models via graphics processing unit (GPU) programming. The developments are partly motivated by computational challenges arising in fitting models of increasing heterogeneity to increasingly large datasets. An example context concerns common biological studies using high-throughput technologies generating many, very large datasets and requiring increasingly high-dimensional mixture models with large numbers of mixture components.We outline important strategies and processes for GPU computation in Bayesian simulation and optimization approaches, give examples of the benefits of GPU implementations in terms of processing speed and scale-up in ability to analyze large datasets, and provide a detailed, tutorial-style exposition that will benefit readers interested in developing GPU-based approaches in other statistical models. Novel, GPU-oriented approaches to modifying existing algorithms software design can lead to vast speed-up and, critically, enable statistical analyses that presently will not be performed due to compute time limitations in traditional computational environments. Supplementalmaterials are provided with all source code, example data, and details that will enable readers to implement and explore the GPU approach in this mixture modeling context. © 2010 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Authors
Suchard, MA; Wang, Q; Chan, C; Frelinger, J; Cron, A; West, M
MLA Citation
Suchard, MA, Wang, Q, Chan, C, Frelinger, J, Cron, A, and West, M. "Understanding GPU programming for statistical computation: Studies in massively parallel massive mixtures." Journal of Computational and Graphical Statistics 19.2 (2010): 419-438.
Website
http://hdl.handle.net/10161/4404
PMID
20877443
Source
scival
Published In
Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
Volume
19
Issue
2
Publish Date
2010
Start Page
419
End Page
438
DOI
10.1198/jcgs.2010.10016

Discriminative information analysis in mixture modelling

Authors
Lin, L; Chan, C; West, M
MLA Citation
Lin, L, Chan, C, and West, M. "Discriminative information analysis in mixture modelling." Working Paper, Department of Statistical Science, Duke University 10-23 (2010). (Academic Article)
Source
manual
Published In
Working Paper, Department of Statistical Science, Duke University
Issue
10-23
Publish Date
2010

Selection sampling from large data sets for targeted inference in mixture modeling

One of the challenges in using Markov chain Monte Carlo for model analysis in studies with very large datasets is the need to scan through the whole data at each iteration of the sampler, which can be computationally prohibitive. Several approaches have been developed to address this, typically drawing compu-tationally manageable subsamples of the data. Here we consider the specific case where most of the data from a mixture model provides little or no information about the parameters of interest, and we aim to select subsamples such that the information extracted is most relevant. The motivating application arises in flow cytometry, where several measurements from a vast number of cells are available. Interest lies in identifying specific rare cell subtypes and characterizing them according to their corresponding markers. We present a Markov chain Monte Carlo approach where an initial subsample of the full dataset is used to guide selection sampling of a further set of observations targeted at a scientifically interesting, low probability region. We define a Sequential Monte Carlo strategy in which the targeted subsample is augmented sequentially as estimates improve, and introduce a stopping rule for determining the size of the targeted subsample. An example from flow cytometry illustrates the ability of the approach to increase the resolution of inferences for rare cell subtypes. © 2010 International Society for Bayesian Analysis.

Authors
Manolopoulou, I; Chan, C; West, M
MLA Citation
Manolopoulou, I, Chan, C, and West, M. "Selection sampling from large data sets for targeted inference in mixture modeling." Bayesian Analysis 5.3 (2010): 429-450.
Source
scival
Published In
Bayesian Analysis
Volume
5
Issue
3
Publish Date
2010
Start Page
429
End Page
450
DOI
10.1214/10-BA517

Rejoinder

We thank the discussants, Fabio Rigat and Nick Whiteley, for their insightful and positive comments. They suggest a number of potential directions for extension of the work and raise connections with other research. We address the points they raise in connection with broader modeling and communication considerations, followed by specific aspects and details of computational strategy. © 2010 International Society for Bayesian Analysis.

Authors
Manolopoulou, I; Chan, C; West, M
MLA Citation
Manolopoulou, I, Chan, C, and West, M. "Rejoinder." Bayesian Analysis 5.3 (2010): 461-464.
Source
scival
Published In
Bayesian Analysis
Volume
5
Issue
3
Publish Date
2010
Start Page
461
End Page
464
DOI
10.1214/10-BA517REJ

Bayesian learning in sparse graphical factor models via variational mean-field annealing

We describe a class of sparse latent factor models, called graphical factor models (GFMs), and relevant sparse learning algorithms for posterior mode estimation. Linear, Gaussian GFMs have sparse, orthogonal factor loadings matrices, that, in addition to sparsity of the implied covariance matrices, also induce conditional independence structures via zeros in the implied precision matrices. We describe the models and their use for robust estimation of sparse latent factor structure and data/signal reconstruction. We develop computational algorithms for model exploration and posterior mode search, addressing the hard combinatorial optimization involved in the search over a huge space of potential sparse configurations. A mean-field variational technique coupled with annealing is developed to successively generate "artificial" posterior distributions that, at the limiting temperature in the annealing schedule, define required posterior modes in the GFM parameter space. Several detailed empirical studies and comparisons to related approaches are discussed, including analyses of handwritten digit image and cancer gene expression data. © 2010 Ryo Yoshida and Mike West.

Authors
Yoshida, R; West, M
MLA Citation
Yoshida, R, and West, M. "Bayesian learning in sparse graphical factor models via variational mean-field annealing." Journal of Machine Learning Research 11 (2010): 1771-1798.
Website
http://hdl.handle.net/10161/4635
PMID
20890391
Source
scival
Published In
Journal of machine learning research : JMLR
Volume
11
Publish Date
2010
Start Page
1771
End Page
1798

Selection Sampling from Large Data Sets for Targeted Inference in Mixture Modeling.

One of the challenges in using Markov chain Monte Carlo for model analysis in studies with very large datasets is the need to scan through the whole data at each iteration of the sampler, which can be computationally prohibitive. Several approaches have been developed to address this, typically drawing computationally manageable subsamples of the data. Here we consider the specific case where most of the data from a mixture model provides little or no information about the parameters of interest, and we aim to select subsamples such that the information extracted is most relevant. The motivating application arises in flow cytometry, where several measurements from a vast number of cells are available. Interest lies in identifying specific rare cell subtypes and characterizing them according to their corresponding markers. We present a Markov chain Monte Carlo approach where an initial subsample of the full dataset is used to guide selection sampling of a further set of observations targeted at a scientifically interesting, low probability region. We define a Sequential Monte Carlo strategy in which the targeted subsample is augmented sequentially as estimates improve, and introduce a stopping rule for determining the size of the targeted subsample. An example from flow cytometry illustrates the ability of the approach to increase the resolution of inferences for rare cell subtypes.

Authors
Manolopoulou, I; Chan, C; West, M
MLA Citation
Manolopoulou, I, Chan, C, and West, M. "Selection Sampling from Large Data Sets for Targeted Inference in Mixture Modeling." Bayesian Anal 5.3 (2010): 1-22.
PMID
20865145
Source
pubmed
Published In
Bayesian Analysis
Volume
5
Issue
3
Publish Date
2010
Start Page
1
End Page
22

The Oxford Handbook of Applied Bayesian Analysis

Bayesian analysis has developed rapidly in applications in the last two decades and research in Bayesian methods remains dynamic and fast-growing. Dramatic advances in modelling concepts and computational technologies now enable routine application of Bayesian analysis using increasingly realistic stochastic models, and this drives the adoption of Bayesian approaches in many areas of science, technology, commerce, and industry. This Handbook explores contemporary Bayesian analysis across a variety of application areas. Chapters written by leading exponents of applied Bayesian analysis showcase the scientific ease and natural application of Bayesian modelling, and present solutions to real, engaging, societally important and demanding problems. The chapters are grouped into five general areas: Biomedical & Health Sciences; Industry, Economics & Finance; Environment & Ecology; Policy, Political & Social Sciences; and Natural & Engineering Sciences, and Appendix material in each touches on key concepts, models, and techniques of the chapter that are also of broader pedagogic and applied interest. Contributors to this volume - Dave Bell; Adelmo Bertolde; R.J. Boys; Carlos Carvalho; Taylan Cemgil; Peter Challenor; Jerry Cheng; Jim Clark; Helen Clough; Thomas Costigliola; Jonathan Cumming; Paul Damien; Philip Dawid; Michael Dietze; David Dunson; Jesus Fernandez-Villaverde; Marco Ferreira; Dani Gamerman; James Gattiker; Alan Gelfand; Simon Godsill; Michael Goldstein; Flavio Goncalves; Robert Gramacy; Genetha Gray; Peter Green; Pablo Guerron-Quintana; Salman Habib; Tim Hanson; Karl Heiner; Katrin Heitmann; D.A. Henderson; Michelle Hersh; David Higdon; Jennifer Hoeting; Scott Holan; Ines Ibanez; Allan James; Michael Jordan; Marc Kennedy; Dan Klein; Shannon LaDeau; Herbert Lee; Percy Liang; Hedibert Lopes; Samantha Low Choy; Joe Lucas; David Madigan; Kanti Mardia; Sean McMahon; Doug McNeall; Kerrie Mengersen; Dan Merl; Jessica Metcalf; Emily Moran; Julia Mortera; Dave Morton; Justine Murray; Charlie Nakhleh; Joseph Nevins; Vysaul Nyirongo; Jeremy Oakley; Anthony O'Hagan; Luke Pangle; Paul Peeling; Joao Batista Pereira; Antonio Pievatolo; Nicholas Polson; Emira Popova; Raquel Prado; C.J. Proctor; Jose Quintana; Jill Rickershauser; Donald Rubin; Juan Rubio-Ramirez; Yann Ruffieux; Fabrizio Ruggeri; Sujit Sahu; Alexandra Schmidt; James Scott; Haige Shen; Richard Smith; Tufi Soares; Matt Taddy; Claudia Tebaldi; Paola Vicard; Pedro Paulo Vieira; Xiaoqin Wang; Mike West; Nick Whiteley; Darren Wilkinson; Mike Wolosin; Li Yin; Elizabeth Zell

MLA Citation
The Oxford Handbook of Applied Bayesian Analysis. Ed. A O' Hagan and M West. 2010.
Source
repec
Publish Date
2010

Spatial Mixture Modelling for Unobserved Point Processes: Examples in Immunofluorescence Histology.

We discuss Bayesian modelling and computational methods in analysis of indirectly observed spatial point processes. The context involves noisy measurements on an underlying point process that provide indirect and noisy data on locations of point outcomes. We are interested in problems in which the spatial intensity function may be highly heterogenous, and so is modelled via flexible nonparametric Bayesian mixture models. Analysis aims to estimate the underlying intensity function and the abundance of realized but unobserved points. Our motivating applications involve immunological studies of multiple fluorescent intensity images in sections of lymphatic tissue where the point processes represent geographical configurations of cells. We are interested in estimating intensity functions and cell abundance for each of a series of such data sets to facilitate comparisons of outcomes at different times and with respect to differing experimental conditions. The analysis is heavily computational, utilizing recently introduced MCMC approaches for spatial point process mixtures and extending them to the broader new context here of unobserved outcomes. Further, our example applications are problems in which the individual objects of interest are not simply points, but rather small groups of pixels; this implies a need to work at an aggregate pixel region level and we develop the resulting novel methodology for this. Two examples with with immunofluorescence histology data demonstrate the models and computational methodology.

Authors
Ji, C; Merl, D; Kepler, TB; West, M
MLA Citation
Ji, C, Merl, D, Kepler, TB, and West, M. "Spatial Mixture Modelling for Unobserved Point Processes: Examples in Immunofluorescence Histology." Bayesian Anal 4.2 (December 4, 2009): 297-316.
PMID
21037943
Source
pubmed
Published In
Bayesian Analysis
Volume
4
Issue
2
Publish Date
2009
Start Page
297
End Page
316

Bayesian analysis of matrix normal graphical models.

We present Bayesian analyses of matrix-variate normal data with conditional independencies induced by graphical model structuring of the characterizing covariance matrix parameters. This framework of matrix normal graphical models includes prior specifications, posterior computation using Markov chain Monte Carlo methods, evaluation of graphical model uncertainty and model structure search. Extensions to matrix-variate time series embed matrix normal graphs in dynamic models. Examples highlight questions of graphical model uncertainty, search and comparison in matrix data contexts. These models may be applied in a number of areas of multivariate analysis, time series and also spatial modelling.

Authors
Wang, H; West, M
MLA Citation
Wang, H, and West, M. "Bayesian analysis of matrix normal graphical models." Biometrika 96.4 (December 2009): 821-834.
PMID
22822246
Source
pubmed
Published In
Biometrika
Volume
96
Issue
4
Publish Date
2009
Start Page
821
End Page
834
DOI
10.1093/biomet/asp049

The genomic analysis of lactic acidosis and acidosis response in human cancers

Authors
Julia, ; Chen, L-Y; Lucas, JE; Schroeder, T; Mori, S; Wu, J; Nevins, J; Dewhirst, M; West, M; Chi, J-T
MLA Citation
Julia, , Chen, L-Y, Lucas, JE, Schroeder, T, Mori, S, Wu, J, Nevins, J, Dewhirst, M, West, M, and Chi, J-T. "The genomic analysis of lactic acidosis and acidosis response in human cancers." May 1, 2009.
Source
wos-lite
Published In
Cancer Research
Volume
69
Publish Date
2009

A genomic strategy to elucidate modules of oncogenic pathway signaling networks.

Recent studies have emphasized the importance of pathway-specific interpretations for understanding the functional relevance of gene alterations in human cancers. Although signaling activities are often conceptualized as linear events, in reality, they reflect the activity of complex functional networks assembled from modules that each respond to input signals. To acquire a deeper understanding of this network structure, we developed an approach to deconstruct pathways into modules represented by gene expression signatures. Our studies confirm that they represent units of underlying biological activity linked to known biochemical pathway structures. Importantly, we show that these signaling modules provide tools to dissect the complexity of oncogenic states that define disease outcomes as well as response to pathway-specific therapeutics. We propose that this model of pathway structure constitutes a framework to study the processes by which information propogates through cellular networks and to elucidate the relationships of fundamental modules to cellular and clinical phenotypes.

Authors
Chang, JT; Carvalho, C; Mori, S; Bild, AH; Gatza, ML; Wang, Q; Lucas, JE; Potti, A; Febbo, PG; West, M; Nevins, JR
MLA Citation
Chang, JT, Carvalho, C, Mori, S, Bild, AH, Gatza, ML, Wang, Q, Lucas, JE, Potti, A, Febbo, PG, West, M, and Nevins, JR. "A genomic strategy to elucidate modules of oncogenic pathway signaling networks." Mol Cell 34.1 (April 10, 2009): 104-114.
PMID
19362539
Source
pubmed
Published In
Molecular Cell
Volume
34
Issue
1
Publish Date
2009
Start Page
104
End Page
114
DOI
10.1016/j.molcel.2009.02.030

Cross-study projections of genomic biomarkers: An evaluation in cancer genomics

Human disease studies using DNA microarrays in both clinical/ observational and experimental/controlled studies are having increasing impact on our understanding of the complexity of human diseases. A fundamental concept is the use of gene expression as a "common currency" that links the results of in vitro controlled experiments to in vivo observational human studies. Many studies - in cancer and other diseases - have shown promise in using in vitro cell manipulations to improve understanding of in vivo biology, but experiments often simply fail to reflect the enormous phenotypic variation seen in human diseases. We address this with a framework and methods to dissect, enhance and extend the in vivo utility of in vitro derived gene expression signatures. From an experimentally defined gene expression signature we use statistical factor analysis to generate multiple quantitative factors in human cancer gene expression data. These factors retain their relationship to the original, one-dimensional in vitro signature but better describe the diversity of in vivo biology. In a breast cancer analysis, we show that factors can reflect fundamentally different biological processes linked to molecular and clinical features of human cancers, and that in combination they can improve prediction of clinical outcomes. © 2009 Lucas et al.

Authors
Lucas, JE; Carvalho, CM; Chen, JLY; Chi, JT; West, M
MLA Citation
Lucas, JE, Carvalho, CM, Chen, JLY, Chi, JT, and West, M. "Cross-study projections of genomic biomarkers: An evaluation in cancer genomics." PLoS ONE 4.2 (February 19, 2009).
Source
scopus
Published In
PloS one
Volume
4
Issue
2
Publish Date
2009
DOI
10.1371/journal.pone.0004523

Hierarchical nonparametric mixture models

Authors
Merl, D; West, M
MLA Citation
Merl, D, and West, M. Hierarchical nonparametric mixture models. Department of Statistical Science, Duke University, 2009.
Source
manual
Publish Date
2009

Dynamic graphical models and portfolio allocations for structured mutual funds

Authors
Reeson, C; Carvalho, CM; West, M
MLA Citation
Reeson, C, Carvalho, CM, and West, M. Dynamic graphical models and portfolio allocations for structured mutual funds. Department of Statistical Science, Duke University, 2009.
Source
manual
Publish Date
2009

A bayesian analysis strategy for cross-study translation of gene expression biomarkers.

We describe a strategy for the analysis of experimentally derived gene expression signatures and their translation to human observational data. Sparse multivariate regression models are used to identify expression signature gene sets representing downstream biological pathway events following interventions in designed experiments. When translated into in vivo human observational data, analysis using sparse latent factor models can yield multiple quantitative factors characterizing expression patterns that are often more complex than in the controlled, in vitro setting. The estimation of common patterns in expression that reflect all aspects of covariation evident in vivo offers an enhanced, modular view of the complexity of biological associations of signature genes. This can identify substructure in the biological process under experimental investigation and improved biomarkers of clinical outcomes. We illustrate the approach in a detailed study from an oncogene intervention experiment where in vivo factor profiling of an in vitro signature generates biological insights related to underlying pathway activities and chromosomal structure, and leads to refinements of cancer recurrence risk stratification across several cancer studies.

Authors
Lucas, J; Carvalho, C; West, M
MLA Citation
Lucas, J, Carvalho, C, and West, M. "A bayesian analysis strategy for cross-study translation of gene expression biomarkers." Stat Appl Genet Mol Biol 8 (2009): Article-11.
PMID
19222378
Source
pubmed
Published In
Statistical Applications in Genetics and Molecular Biology
Volume
8
Publish Date
2009
Start Page
Article
End Page
11
DOI
10.2202/1544-6115.1436

Cross-study projections of genomic biomarkers: an evaluation in cancer genomics.

Human disease studies using DNA microarrays in both clinical/observational and experimental/controlled studies are having increasing impact on our understanding of the complexity of human diseases. A fundamental concept is the use of gene expression as a "common currency" that links the results of in vitro controlled experiments to in vivo observational human studies. Many studies--in cancer and other diseases--have shown promise in using in vitro cell manipulations to improve understanding of in vivo biology, but experiments often simply fail to reflect the enormous phenotypic variation seen in human diseases. We address this with a framework and methods to dissect, enhance and extend the in vivo utility of in vitro derived gene expression signatures. From an experimentally defined gene expression signature we use statistical factor analysis to generate multiple quantitative factors in human cancer gene expression data. These factors retain their relationship to the original, one-dimensional in vitro signature but better describe the diversity of in vivo biology. In a breast cancer analysis, we show that factors can reflect fundamentally different biological processes linked to molecular and clinical features of human cancers, and that in combination they can improve prediction of clinical outcomes.

Authors
Lucas, JE; Carvalho, CM; Chen, JL-Y; Chi, J-T; West, M
MLA Citation
Lucas, JE, Carvalho, CM, Chen, JL-Y, Chi, J-T, and West, M. "Cross-study projections of genomic biomarkers: an evaluation in cancer genomics." PLoS One 4.2 (2009): e4523-.
PMID
19225561
Source
pubmed
Published In
PloS one
Volume
4
Issue
2
Publish Date
2009
Start Page
e4523
DOI
10.1371/journal.pone.0004523

A dynamic modelling strategy for bayesian computer model emulation

Computer model evaluation studies build statistical models of deterministic simulation-based predictions of field data to then assess and criticize the computer model and suggest refinements. Computer models are often expensive computationally: statistical models that adequately emulate their key features can be very much more efficient. Gaussian process models are often used as emulators, but the resulting computations lack the ability to scale to higher-dimensional, time-dependent or functional outputs. For some such problems, especially for contexts of time series outputs, building emulators using dynamic linear models provides a computationally attractive alternative as well as a flexible modelling approach capable of emulating a broad range of stochastic structures underlying the input-output simulations. We describe this here, combining Bayesian multivariate dynamic linear models with Gaussian process modelling in an effective manner, and illustrate the approach with data from a hydrological simulation model. The general strategy will be useful for other computer model evaluation studies with time series or functional outputs. © 2009 International Society for Bayesian Analysis.

Authors
Liu, F; West, M
MLA Citation
Liu, F, and West, M. "A dynamic modelling strategy for bayesian computer model emulation." Bayesian Analysis 4.2 (2009): 393-412.
Source
scival
Published In
Bayesian Analysis
Volume
4
Issue
2
Publish Date
2009
Start Page
393
End Page
412
DOI
10.1214/09-BA415

AN INTEGRATIVE ANALYSIS OF CANCER GENE EXPRESSION STUDIES USING BAYESIAN LATENT FACTOR MODELING.

We present an applied study in cancer genomics for integrating data and inferences from laboratory experiments on cancer cell lines with observational data obtained from human breast cancer studies. The biological focus is on improving understanding of transcriptional responses of tumors to changes in the pH level of the cellular microenvironment. The statistical focus is on connecting experimentally defined biomarkers of such responses to clinical outcome in observational studies of breast cancer patients. Our analysis exemplifies a general strategy for accomplishing this kind of integration across contexts. The statistical methodologies employed here draw heavily on Bayesian sparse factor models for identifying, modularizing and correlating with clinical outcome these signatures of aggregate changes in gene expression. By projecting patterns of biological response linked to specific experimental interventions into observational studies where such responses may be evidenced via variation in gene expression across samples, we are able to define biomarkers of clinically relevant physiological states and outcomes that are rooted in the biology of the original experiment. Through this approach we identify microenvironment-related prognostic factors capable of predicting long term survival in two independent breast cancer datasets. These results suggest possible directions for future laboratory studies, as well as indicate the potential for therapeutic advances though targeted disruption of specific pathway components.

Authors
Merl, D; Chen, JL-Y; Chi, J-T; West, M
MLA Citation
Merl, D, Chen, JL-Y, Chi, J-T, and West, M. "AN INTEGRATIVE ANALYSIS OF CANCER GENE EXPRESSION STUDIES USING BAYESIAN LATENT FACTOR MODELING." Ann Appl Stat 3.4 (2009): 1675-1694.
PMID
20953268
Source
pubmed
Published In
The annals of applied statistics
Volume
3
Issue
4
Publish Date
2009
Start Page
1675
End Page
1694

Sequential Monte Carlo in model comparison: Example in cellular dynamics in systems biology

Authors
Mukherjee, C; West, M
MLA Citation
Mukherjee, C, and West, M. "Sequential Monte Carlo in model comparison: Example in cellular dynamics in systems biology." JSM Proceedings,Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association (2009): 1274-1287.
Source
manual
Published In
JSM Proceedings,Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association
Publish Date
2009
Start Page
1274
End Page
1287

The genomic analysis of lactic acidosis and acidosis response in human cancers.

The tumor microenvironment has a significant impact on tumor development. Two important determinants in this environment are hypoxia and lactic acidosis. Although lactic acidosis has long been recognized as an important factor in cancer, relatively little is known about how cells respond to lactic acidosis and how that response relates to cancer phenotypes. We develop genome-scale gene expression studies to dissect transcriptional responses of primary human mammary epithelial cells to lactic acidosis and hypoxia in vitro and to explore how they are linked to clinical tumor phenotypes in vivo. The resulting experimental signatures of responses to lactic acidosis and hypoxia are evaluated in a heterogeneous set of breast cancer datasets. A strong lactic acidosis response signature identifies a subgroup of low-risk breast cancer patients having distinct metabolic profiles suggestive of a preference for aerobic respiration. The association of lactic acidosis response with good survival outcomes may relate to the role of lactic acidosis in directing energy generation toward aerobic respiration and utilization of other energy sources via inhibition of glycolysis. This "inhibition of glycolysis" phenotype in tumors is likely caused by the repression of glycolysis gene expression and Akt inhibition. Our study presents a genomic evaluation of the prognostic information of a lactic acidosis response independent of the hypoxic response. Our results identify causal roles of lactic acidosis in metabolic reprogramming, and the direct functional consequence of lactic acidosis pathway activity on cellular responses and tumor development. The study also demonstrates the utility of genomic analysis that maps expression-based findings from in vitro experiments to human samples to assess links to in vivo clinical phenotypes.

Authors
Chen, JL-Y; Lucas, JE; Schroeder, T; Mori, S; Wu, J; Nevins, J; Dewhirst, M; West, M; Chi, J-T
MLA Citation
Chen, JL-Y, Lucas, JE, Schroeder, T, Mori, S, Wu, J, Nevins, J, Dewhirst, M, West, M, and Chi, J-T. "The genomic analysis of lactic acidosis and acidosis response in human cancers." PLoS Genet 4.12 (December 2008): e1000293-.
PMID
19057672
Source
pubmed
Published In
PLoS genetics
Volume
4
Issue
12
Publish Date
2008
Start Page
e1000293
DOI
10.1371/journal.pgen.1000293

Statistical mixture modeling for cell subtype identification in flow cytometry.

Statistical mixture modeling provides an opportunity for automated identification and resolution of cell subtypes in flow cytometric data. The configuration of cells as represented by multiple markers simultaneously can be modeled arbitrarily well as a mixture of Gaussian distributions in the dimension of the number of markers. Cellular subtypes may be related to one or multiple components of such mixtures, and fitted mixture models can be evaluated in the full set of markers as an alternative, or adjunct, to traditional subjective gating methods that rely on choosing one or two dimensions. Four color flow data from human blood cells labeled with FITC-conjugated anti-CD3, PE-conjugated anti-CD8, PE-Cy5-conjugated anti-CD4, and APC-conjugated anti-CD19 Abs was acquired on a FACSCalibur. Cells from four murine cell lines, JAWS II, RAW 264.7, CTLL-2, and A20, were also stained with FITC-conjugated anti-CD11c, PE-conjugated anti-CD11b, PE-Cy5-conjugated anti-CD8a, and PE-Cy7-conjugated-CD45R/B220 Abs, respectively, and single color flow data were collected on an LSRII. The data were fitted with a mixture of multivariate Gaussians using standard Bayesian statistical approaches and Markov chain Monte Carlo computations. Statistical mixture models were able to identify and purify major cell subsets in human peripheral blood, using an automated process that can be generalized to an arbitrary number of markers. Validation against both traditional expert gating and synthetic mixtures of murine cell lines with known mixing proportions was also performed. This article describes the studies of statistical mixture modeling of flow cytometric data, and demonstrates their utility in examples with four-color flow data from human peripheral blood samples and synthetic mixtures of murine cell lines.

Authors
Chan, C; Feng, F; Ottinger, J; Foster, D; West, M; Kepler, TB
MLA Citation
Chan, C, Feng, F, Ottinger, J, Foster, D, West, M, and Kepler, TB. "Statistical mixture modeling for cell subtype identification in flow cytometry." Cytometry A 73.8 (August 2008): 693-701.
PMID
18496851
Source
pubmed
Published In
Cytometry
Volume
73
Issue
8
Publish Date
2008
Start Page
693
End Page
701
DOI
10.1002/cyto.a.20583

Of cardiovascular illness and diversity of biological response.

Noise in gene expression (stochastic variation in the composition of the transcriptome in response to stimuli) may play an important role in maintaining robustness and flexibility, which ensure the stability of normal physiology and provide adaptability to environmental changes for the living system. Broad-based technologies have allowed us to study with unprecedented accuracy the molecular profiles of various states of health and cardiovascular disease. In doing so, we have observed a correlation between the degree of variation in gene expression and the state of health. Specifically, the stochastic variation in gene expression in response to environmental and physiological factors is found in healthy mice, and tends to disappear in mice with advanced disease states. Although further evidence is needed to draw a solid conclusion with respect to the significance of decreased transcriptional noise in the disease state as a whole, it is tantalizing to introduce the concept that stochasticity may be linked to the organism's adaptability to a changing environment, and the "quiet" states of gene expression may indicate the loss of diversity in the organism's response.

Authors
Goldschmidt-Clermont, PJ; Dong, C; West, M; Seo, DM
MLA Citation
Goldschmidt-Clermont, PJ, Dong, C, West, M, and Seo, DM. "Of cardiovascular illness and diversity of biological response." Trends Cardiovasc Med 18.5 (July 2008): 194-197. (Review)
PMID
18790390
Source
pubmed
Published In
Trends in Cardiovascular Medicine
Volume
18
Issue
5
Publish Date
2008
Start Page
194
End Page
197
DOI
10.1016/j.tcm.2008.07.003

CellTracer: Software for automated image segmentation and lineage mapping for single-cell studies

Authors
Wang, Q; You, L; West, M
MLA Citation
Wang, Q, You, L, and West, M. CellTracer: Software for automated image segmentation and lineage mapping for single-cell studies. Department of Statistical Science, Duke University, 2008.
Source
manual
Publish Date
2008

High-dimensional sparse factor modeling: Applications in gene expression genomics

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived "factors" as representing biological "subpathway" structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology. © 2008 American Statistical Association.

Authors
Carvalho, CM; Chang, J; Lucas, JE; Nevins, JR; Wang, Q; West, M
MLA Citation
Carvalho, CM, Chang, J, Lucas, JE, Nevins, JR, Wang, Q, and West, M. "High-dimensional sparse factor modeling: Applications in gene expression genomics." Journal of the American Statistical Association 103.484 (2008): 1438-1456.
PMID
21218139
Source
scival
Published In
Journal of the American Statistical Association
Volume
103
Issue
484
Publish Date
2008
Start Page
1438
End Page
1456
DOI
10.1198/016214508000000869

Genomic analysis of response to lactic acidosis and acidosis in human cancers

Authors
Chen, JL; Lucas, JE; Schroeder, T; Mori, S; Wu, J; Nevins, JR; Dewhirst, M; West, M; Chi, J-TA
MLA Citation
Chen, JL, Lucas, JE, Schroeder, T, Mori, S, Wu, J, Nevins, JR, Dewhirst, M, West, M, and Chi, J-TA. "Genomic analysis of response to lactic acidosis and acidosis in human cancers." PLoS Genetics 4.12 (2008): e1000293-. (Academic Article)
Source
manual
Published In
PLoS Genetics
Volume
4
Issue
12
Publish Date
2008
Start Page
e1000293

Statistical mixture modelling for cell subtype identification in flow cytometry

Authors
Chan, C; Feng, F; Ottinger, J; Foster, D; West, M; Kepler, TB
MLA Citation
Chan, C, Feng, F, Ottinger, J, Foster, D, West, M, and Kepler, TB. "Statistical mixture modelling for cell subtype identification in flow cytometry." Cytometry, A 73 (2008): 693-701. (Academic Article)
Source
manual
Published In
Cytometry, A
Volume
73
Publish Date
2008
Start Page
693
End Page
701

Characterizing disease-specific pathways and their coordination by integrative microarray analysis with application to cancer

Authors
Xu, M; Kao, MCJ; Nunez-Iglesias, J; Nevins, JR; West, M; Zhou, XJ
MLA Citation
Xu, M, Kao, MCJ, Nunez-Iglesias, J, Nevins, JR, West, M, and Zhou, XJ. "Characterizing disease-specific pathways and their coordination by integrative microarray analysis with application to cancer." BMC Genomics 9 (2008): Supp 1:S12-Supp 1:S12. (Academic Article)
Source
manual
Published In
BMC Genomics
Volume
9
Publish Date
2008
Start Page
Supp 1:S12
End Page
Supp 1:S12

An integrative approach to characterize disease-specific pathways and their coordination: A case study in cancer

Background: The most common application of microarray technology in disease research is to identify genes differentially expressed in disease versus normal tissues. However, it is known that, in complex diseases, phenotypes are determined not only by genes, but also by the underlying structure of genetic networks. Often, it is the interaction of many genes that causes phenotypic variations. Results: In this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules. We propose an unsupervised method that take into account both co-expression dynamics and network topological information to simultaneously infer network modules and phenotype conditions in which they are activated or de-activated. Using our method, we have discovered network modules specific to cancer or subtypes of cancers. Many of these modules are consistent with or supported by their functional annotations or their previously known involvement in cancer. In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression. While individual components of this module have been suggested to be associated with tumor suppression, their coordinated function has never been elucidated. Here by adopting a network perspective, we have identified their interrelationships and, particularly, a hub gene PDGFRL that may play an important role in this tumor suppressor network. Conclusion: Using a network-based approach, our method provides new insights into the complex cellular mechanisms that characterize cancer and cancer subtypes. By incorporating co-expression dynamics information, our approach can not only extract more functionally homogeneous modules than those based solely on network topology, but also reveal pathway coordination beyond co-expression. © 2008 Xu et al.; licensee BioMed Central Ltd.

Authors
Xu, M; Kao, M-CJ; Nunez-Iglesias, J; Nevins, JR; West, M; Jasmine, XJ
MLA Citation
Xu, M, Kao, M-CJ, Nunez-Iglesias, J, Nevins, JR, West, M, and Jasmine, XJ. "An integrative approach to characterize disease-specific pathways and their coordination: A case study in cancer." BMC Genomics 9.SUPPL. 1 (2008).
PMID
18366601
Source
scival
Published In
BMC Genomics
Volume
9
Issue
SUPPL. 1
Publish Date
2008
DOI
10.1186/1471-2164-9-S1-S12

Bayesian Weibull tree models for survival analysis of clinico-genomic data

An important goal of research involving gene expression data for outcome prediction is to establish the ability of genomic data to define clinically relevant risk factors. Recent studies have demonstrated that microarray data can successfully cluster patients into low- and high-risk categories. However, the need exists for models which examine how genomic predictors interact with existing clinical factors and provide personalized outcome predictions. We have developed clinico-genomic tree models for survival outcomes which use recursive partitioning to subdivide the current data set into homogeneous subgroups of patients, each with a specific Weibull survival distribution. These trees can provide personalized predictive distributions of the probability of survival for individuals of interest. Our strategy is to fit multiple models; within each model we adopt a prior on the Weibull scale parameter and update this prior via Empirical Bayes whenever the sample is split at a given node. The decision to split is based on a Bayes factor criterion. The resulting trees are weighted according to their relative likelihood values and predictions are made by averaging over models. In a pilot study of survival in advanced stage ovarian cancer we demonstrate that clinical and genomic data are complementary sources of information relevant to survival, and we use the exploratory nature of the trees to identify potential genomic biomarkers worthy of further study. © 2007 Elsevier B.V. All rights reserved.

Authors
Clarke, J; West, M
MLA Citation
Clarke, J, and West, M. "Bayesian Weibull tree models for survival analysis of clinico-genomic data." Statistical Methodology 5.3 (2008): 238-262.
Source
scival
Published In
Statistical Methodology
Volume
5
Issue
3
Publish Date
2008
Start Page
238
End Page
262
DOI
10.1016/j.stamet.2007.09.003

Understanding the use of unlabelled data in predictive modelling

Authors
Liang, F; Mukherjee, S; West, M
MLA Citation
Liang, F, Mukherjee, S, and West, M. "Understanding the use of unlabelled data in predictive modelling." Statistical Science 22.2 (October 2007): 189-205. (Academic Article)
Source
manual
Published In
Statistical Science
Volume
22
Issue
2
Publish Date
2007
Start Page
189
End Page
205

OF MICE AND MEN: SPARSE STATISTICAL MODELING IN CARDIOVASCULAR GENOMICS

Authors
Seo, DM; Goldschmidt-Clermont, PJ; West, M
MLA Citation
Seo, DM, Goldschmidt-Clermont, PJ, and West, M. "OF MICE AND MEN: SPARSE STATISTICAL MODELING IN CARDIOVASCULAR GENOMICS." ANNALS OF APPLIED STATISTICS 1.1 (June 2007): 152-178.
Source
wos-lite
Published In
The annals of applied statistics
Volume
1
Issue
1
Publish Date
2007
Start Page
152
End Page
178
DOI
10.1214/07-AOAS110

Dynamic matrix-variate graphical models

Authors
Carvalho, CM; West, M
MLA Citation
Carvalho, CM, and West, M. "Dynamic matrix-variate graphical models." Bayesian Analysis 2 (2007): 69-98. (Academic Article)
Source
manual
Published In
Bayesian Analysis
Volume
2
Publish Date
2007
Start Page
69
End Page
98

Non-parametric Bayesian kernel models

Authors
Liang, F; Mao, K; Liao, M; Mukherjee, S; West, M
MLA Citation
Liang, F, Mao, K, Liao, M, Mukherjee, S, and West, M. "Non-parametric Bayesian kernel models." Department of Statistical Science, Duke University (2007).
Source
manual
Published In
Department of Statistical Science, Duke University
Publish Date
2007

Dynamic matrix-variate graphical models

This paper introduces a novel class of Bayesian models for multivariate time series analysis based on a synthesis of dynamic linear models and graphical models. The synthesis uses sparse graphical modelling ideas to introduce struc-tured, conditional independence relationships in the time-varying, cross-sectional covariance matrices of multiple time series. We define this new class of models and their theoretical structure involving novel matrix-normal/hyper-inverse Wishart distributions. We then describe the resulting Bayesian methodology and compu-tational strategies for model fitting and prediction. This includes novel stochastic evolution theory for time-varying, structured variance matrices, and the full se-quential and conjugate updating, filtering and forecasting analysis. The models are then applied in the context of financial time series for predictive portfolio analysis. The improvements defined in optimal Bayesian decision analysis in this example context vividly illustrate the practical benefits of the parsimony induced via appro-priate graphical model structuring in multivariate dynamic modelling. We discuss theoretical and empirical aspects of the conditional independence structures in such models, issues of model uncertainty and search, and the relevance of this new framework as a key step towards scaling multivariate dynamic Bayesian modelling methodology to time series of increasing dimension and complexity. © 2007 International Society for Bayesian Analysis.

Authors
Carvalho, CM; West, M
MLA Citation
Carvalho, CM, and West, M. "Dynamic matrix-variate graphical models." Bayesian Analysis 2.1 (2007): 69-98.
Source
scival
Published In
Bayesian Analysis
Volume
2
Issue
1
Publish Date
2007
Start Page
69
End Page
98
DOI
10.1214/07-BA204

Bayesian CART: Prior structure and MCMC computations

Authors
Wu, Y; Tjelmeland, H; West, M
MLA Citation
Wu, Y, Tjelmeland, H, and West, M. "Bayesian CART: Prior structure and MCMC computations." Journal of Computational and Graphical Statistics 16 (2007): 44-66. (Academic Article)
Source
manual
Published In
Journal of Computational and Graphical Statistics
Volume
16
Publish Date
2007
Start Page
44
End Page
66

Shotgun stochastic search in regression with many predictors

Authors
Hans, C; Dobra, A; West, M
MLA Citation
Hans, C, Dobra, A, and West, M. "Shotgun stochastic search in regression with many predictors." Journal of the American Statistical Association 102 (2007): 507-516. (Academic Article)
Source
manual
Published In
Journal of the American Statistical Association
Volume
102
Publish Date
2007
Start Page
507
End Page
516

Simulation of hyper-inverse Wishart distributions in graphical models

We introduce and exemplify an efficient method for direct sampling from hyper-inverse Wishart distributions. The method relies very naturally on the use of standard junction-tree representation of graphs, and couples these with matrix results for inverse Wishart distributions. We describe the theory and resulting computational algorithms for both decomposable and nondecomposable graphical models. An example drawn from financial time series demonstrates application in a context where inferences on a structured covariance model are required. We discuss and investigate questions of scalability of the simulation methods to higher-dimensional distributions. The paper concludes with general comments about the approach, including its use in connection with existing Markov chain Monte Carlo methods that deal with uncertainty about the graphical model structure. © 2007 Biometrika Trust.

Authors
Carvalho, CM; Massam, H; West, M
MLA Citation
Carvalho, CM, Massam, H, and West, M. "Simulation of hyper-inverse Wishart distributions in graphical models." Biometrika 94.3 (2007): 647-659.
Source
scival
Published In
Biometrika
Volume
94
Issue
3
Publish Date
2007
Start Page
647
End Page
659
DOI
10.1093/biomet/asm056

SSS: High-dimensional Bayesian regression model search

Authors
Hans, C; Wang, Q; Dobra, A; West, M
MLA Citation
Hans, C, Wang, Q, Dobra, A, and West, M. "SSS: High-dimensional Bayesian regression model search." Bulletin of the International Society for Bayesian Analysis 24 (2007): 8-9. (Academic Article)
Source
manual
Published In
Bulletin of the International Society for Bayesian Analysis
Volume
24
Publish Date
2007
Start Page
8
End Page
9

BFRM: Bayesian factor regression modelling

Authors
Wang, Q; Carvalho, CM; Lucas, JE; West, M
MLA Citation
Wang, Q, Carvalho, CM, Lucas, JE, and West, M. "BFRM: Bayesian factor regression modelling." Bulletin of the International Society for Bayesian Analysis 14 (2007): 4-5. (Academic Article)
Source
manual
Published In
Bulletin of the International Society for Bayesian Analysis
Volume
14
Publish Date
2007
Start Page
4
End Page
5

The use of unlabeled data in predictive modeling

The incorporation of unlabeled data in regression and classification analysis is an increasing focus of the applied statistics and machine learning literatures, with a number of recent examples demonstrating the potential for unlabeled data to contribute to improved predictive accuracy. The statistical basis for this semisupervised analysis does not appear to have been well delineated; as a result, the underlying theory and rationale may be underappreciated, especially by nonstatisticians. There is also room for statisticians to become more fully engaged in the vigorous research in this important area of intersection of the statistical and computer sciences. Much of the theoretical work in the literature has focused, for example, on geometric and structural properties of the unlabeled data in the context of particular algorithms, rather than probabilistic and statistical questions. This paper overviews the fundamental statistical foundations for predictive modeling and the general questions associated with unlabeled data, highlighting the relevance of venerable concepts of sampling design and prior specification. This theory, illustrated with a series of central illustrative examples and two substantial real data analyses, shows precisely when, why and how unlabeled data matter. © Institute of Mathematical Statistics, 2007.

Authors
Liang, F; Mukherjee, S; West, M
MLA Citation
Liang, F, Mukherjee, S, and West, M. "The use of unlabeled data in predictive modeling." Statistical Science 22.2 (2007): 189-205.
Source
scival
Published In
Statistical science : a review journal of the Institute of Mathematical Statistics
Volume
22
Issue
2
Publish Date
2007
Start Page
189
End Page
205
DOI
10.1214/088342307000000032

Shotgun stochastic search for "large p" regression

Model search in regression with very large numbers of candidate predictors raises challenges for both model specification and computation, for which standard approaches such as Markov chain Monte Carlo (MCMC) methods are often infeasible or ineffective. We describe a novel shotgun stochastic search (SSS) approach that explores "interesting" regions of the resulting high-dimensional model spaces and quickly identifies regions of high posterior probability over models. We describe algorithmic and modeling aspects, priors over the model space that induce sparsity and parsimony over and above the traditional dimension penalization implicit in Bayesian and likelihood analyses, and parallel computation using cluster computers. We discuss an example from gene expression cancer genomics, comparisons with MCMC and other methods, and theoretical and simulation-based aspects of performance characteristics in large-scale regression model searches. We also provide software implementing the methods. © 2007 American Statistical Association.

Authors
Hans, C; Dobra, A; West, M
MLA Citation
Hans, C, Dobra, A, and West, M. "Shotgun stochastic search for "large p" regression." Journal of the American Statistical Association 102.478 (2007): 507-516.
Source
scival
Published In
Journal of the American Statistical Association
Volume
102
Issue
478
Publish Date
2007
Start Page
507
End Page
516
DOI
10.1198/016214507000000121

Bayesian CART: Prior specification and posterior simulation

We present advances in Bayesian modeling and computation for CART (classification and regression tree) models. The modeling innovations include a formal prior distributional structure for tree generation - the pinball prior - that allows for the combination of an explicit specification of a distribution for both the tree size and the tree shape. The core computational innovations involve a novel Metropolis-Hastings method that can dramatically improve the convergence and mixing properties of MCMC methods of Bayesian CART analysis. Earlier MCMC methods have simulated Bayesian CART models using very local MCMC moves, proposing only small changes to a "current" CART model. Our new Metropolis-Hastings move makes large changes in the CART tree, but is at the same time local in that it leaves unchanged the partition of observations into terminal nodes. We evaluate the effectiveness of the proposed algorithm in two examples, one with a constructed data set and one concerning analysis of a published breast cancer dataset. © 2007 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Authors
Wu, Y; Tjelmeland, H; West, M
MLA Citation
Wu, Y, Tjelmeland, H, and West, M. "Bayesian CART: Prior specification and posterior simulation." Journal of Computational and Graphical Statistics 16.1 (2007): 44-66.
Source
scival
Published In
Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
Volume
16
Issue
1
Publish Date
2007
Start Page
44
End Page
66
DOI
10.1198/106186007X180426

Genomic prediction of locoregional recurrence after mastectomy in breast cancer.

PURPOSE: This study aims to explore gene expression profiles that are associated with locoregional (LR) recurrence in breast cancer after mastectomy. PATIENTS AND METHODS: A total of 94 breast cancer patients who underwent mastectomy between 1990 and 2001 and had DNA microarray study on the primary tumor tissues were chosen for this study. Eligible patient should have no evidence of LR recurrence without postmastectomy radiotherapy (PMRT) after a minimum of 3-year follow-up (n = 67) and any LR recurrence (n = 27). They were randomly split into training and validation sets. Statistical classification tree analysis and proportional hazards models were developed to identify and validate gene expression profiles that relate to LR recurrence. RESULTS: Our study demonstrates two sets of gene expression profiles (one with 258 genes and the other 34 genes) to be of predictive value with respect to LR recurrence. The overall accuracy of the prediction tree model in validation sets is estimated 75% to 78%. Of patients in validation data set, the 3-year LR control rate with predictive index more than 0.8 derived from 34-gene prediction models is 91%, and predictive index 0.8 or less is 40% (P = .008). Multivariate analysis of all patients reveals that estrogen receptor and genomic predictive index are independent prognostic factors that affect LR control. CONCLUSION: Using gene expression profiles to develop prediction tree models effectively identifies breast cancer patients who are at higher risk for LR recurrence. This gene expression-based predictive index can be used to select patients for PMRT.

Authors
Cheng, SH; Horng, C-F; West, M; Huang, E; Pittman, J; Tsou, M-H; Dressman, H; Chen, C-M; Tsai, SY; Jian, JJ; Liu, M-C; Nevins, JR; Huang, AT
MLA Citation
Cheng, SH, Horng, C-F, West, M, Huang, E, Pittman, J, Tsou, M-H, Dressman, H, Chen, C-M, Tsai, SY, Jian, JJ, Liu, M-C, Nevins, JR, and Huang, AT. "Genomic prediction of locoregional recurrence after mastectomy in breast cancer." J Clin Oncol 24.28 (October 1, 2006): 4594-4602.
PMID
17008701
Source
pubmed
Published In
Journal of Clinical Oncology
Volume
24
Issue
28
Publish Date
2006
Start Page
4594
End Page
4602
DOI
10.1200/JCO.2005.02.5676

Embracing the complexity of genomic data for personalized medicine.

Numerous recent studies have demonstrated the use of genomic data, particularly gene expression signatures, as clinical prognostic factors in cancer and other complex diseases. Such studies herald the future of genomic medicine and the opportunity for personalized prognosis in a variety of clinical contexts that utilizes genome-scale molecular information. The scale, complexity, and information content of high-throughput gene expression data, as one example of complex genomic information, is often under-appreciated as many analyses continue to focus on defining individual rather than multiplex biomarkers for patient stratification. Indeed, this complexity of genomic data is often--rather paradoxically--viewed as a barrier to its utility. To the contrary, the complexity and scale of global genomic data, as representing the many dimensions of biology, must be embraced for the development of more precise clinical prognostics. The need is for integrated analyses--approaches that embrace the complexity of genomic data, including multiple forms of genomic data, and aim to explore and understand multiple, interacting, and potentially conflicting predictors of risk, rather than continuing on the current and traditional path that oversimplifies and ignores the information content in the complexity. All forms of potentially relevant data should be examined, with particular emphasis on understanding the interactions, complementarities, and possible conflicts among gene expression, genetic, and clinical markers of risk.

Authors
West, M; Ginsburg, GS; Huang, AT; Nevins, JR
MLA Citation
West, M, Ginsburg, GS, Huang, AT, and Nevins, JR. "Embracing the complexity of genomic data for personalized medicine." Genome Res 16.5 (May 2006): 559-566. (Review)
PMID
16651662
Source
pubmed
Published In
Genome research
Volume
16
Issue
5
Publish Date
2006
Start Page
559
End Page
566
DOI
10.1101/gr.3851306

Prognostic index score and clinical prediction model of local regional recurrence after mastectomy in breast cancer patients.

PURPOSE: To develop clinical prediction models for local regional recurrence (LRR) of breast carcinoma after mastectomy that will be superior to the conventional measures of tumor size and nodal status. METHODS AND MATERIALS: Clinical information from 1,010 invasive breast cancer patients who had primary modified radical mastectomy formed the database of the training and testing of clinical prognostic and prediction models of LRR. Cox proportional hazards analysis and Bayesian tree analysis were the core methodologies from which these models were built. To generate a prognostic index model, 15 clinical variables were examined for their impact on LRR. Patients were stratified by lymph node involvement (<4 vs. >or =4) and local regional status (recurrent vs. control) and then, within strata, randomly split into training and test data sets of equal size. To establish prediction tree models, 255 patients were selected by the criteria of having had LRR (53 patients) or no evidence of LRR without postmastectomy radiotherapy (PMRT) (202 patients). RESULTS: With these models, patients can be divided into low-, intermediate-, and high-risk groups on the basis of axillary nodal status, estrogen receptor status, lymphovascular invasion, and age at diagnosis. In the low-risk group, there is no influence of PMRT on either LRR or survival. For intermediate-risk patients, PMRT improves LR control but not metastases-free or overall survival. For the high-risk patients, however, PMRT improves both LR control and metastasis-free and overall survival. CONCLUSION: The prognostic score and predictive index are useful methods to estimate the risk of LRR in breast cancer patients after mastectomy and for estimating the potential benefits of PMRT. These models provide additional information criteria for selection of patients for PMRT, compared with the traditional selection criteria of nodal status and tumor size.

Authors
Cheng, SH; Horng, C-F; Clarke, JL; Tsou, M-H; Tsai, SY; Chen, C-M; Jian, JJ; Liu, M-C; West, M; Huang, AT; Prosnitz, LR
MLA Citation
Cheng, SH, Horng, C-F, Clarke, JL, Tsou, M-H, Tsai, SY, Chen, C-M, Jian, JJ, Liu, M-C, West, M, Huang, AT, and Prosnitz, LR. "Prognostic index score and clinical prediction model of local regional recurrence after mastectomy in breast cancer patients." Int J Radiat Oncol Biol Phys 64.5 (April 1, 2006): 1401-1409.
PMID
16472935
Source
pubmed
Published In
International Journal of Radiation Oncology, Biology, Physics
Volume
64
Issue
5
Publish Date
2006
Start Page
1401
End Page
1409
DOI
10.1016/j.ijrobp.2005.11.015

Gene expression profiles of multiple breast cancer phenotypes and response to neoadjuvant chemotherapy.

PURPOSE: Breast cancer is a heterogeneous disease, and markers for disease subtypes and therapy response remain poorly defined. For that reason, we employed a prospective neoadjuvant study in locally advanced breast cancer to identify molecular signatures of gene expression correlating with known prognostic clinical phenotypes, such as inflammatory breast cancer or the presence of hypoxia. In addition, we defined molecular signatures that correlate with response to neoadjuvant chemotherapy. EXPERIMENTAL DESIGN: Tissue was collected under ultrasound guidance from patients with stage IIB/III breast cancer before four cycles of neoadjuvant liposomal doxorubicin paclitaxel chemotherapy combined with local whole breast hyperthermia. Gene expression analysis was done using Affymetrix U133 Plus 2.0 GeneChip arrays. RESULTS: Gene expression patterns were identified that defined the phenotypes of inflammatory breast cancer as well as tumor hypoxia. In addition, molecular signatures were identified that predicted the persistence of malignancy in the axillary lymph nodes after neoadjuvant chemotherapy. This persistent lymph node signature significantly correlated with disease-free survival in two separate large populations of breast cancer patients. CONCLUSIONS: Gene expression signatures have the capacity to identify clinically significant features of breast cancer and can predict which individual patients are likely to be resistant to neoadjuvant therapy, thus providing the opportunity to guide treatment decisions.

Authors
Dressman, HK; Hans, C; Bild, A; Olson, JA; Rosen, E; Marcom, PK; Liotcheva, VB; Jones, EL; Vujaskovic, Z; Marks, J; Dewhirst, MW; West, M; Nevins, JR; Blackwell, K
MLA Citation
Dressman, HK, Hans, C, Bild, A, Olson, JA, Rosen, E, Marcom, PK, Liotcheva, VB, Jones, EL, Vujaskovic, Z, Marks, J, Dewhirst, MW, West, M, Nevins, JR, and Blackwell, K. "Gene expression profiles of multiple breast cancer phenotypes and response to neoadjuvant chemotherapy." Clin Cancer Res 12.3 Pt 1 (February 1, 2006): 819-826.
PMID
16467094
Source
pubmed
Published In
Clinical cancer research : an official journal of the American Association for Cancer Research
Volume
12
Issue
3 Pt 1
Publish Date
2006
Start Page
819
End Page
826
DOI
10.1158/1078-0432.CCR-05-1447

Oncogenic pathway signatures in human cancers as a guide to targeted therapies.

The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.

Authors
Bild, AH; Yao, G; Chang, JT; Wang, Q; Potti, A; Chasse, D; Joshi, M-B; Harpole, D; Lancaster, JM; Berchuck, A; Olson, JA; Marks, JR; Dressman, HK; West, M; Nevins, JR
MLA Citation
Bild, AH, Yao, G, Chang, JT, Wang, Q, Potti, A, Chasse, D, Joshi, M-B, Harpole, D, Lancaster, JM, Berchuck, A, Olson, JA, Marks, JR, Dressman, HK, West, M, and Nevins, JR. "Oncogenic pathway signatures in human cancers as a guide to targeted therapies." Nature 439.7074 (January 19, 2006): 353-357.
PMID
16273092
Source
pubmed
Published In
Nature
Volume
439
Issue
7074
Publish Date
2006
Start Page
353
End Page
357
DOI
10.1038/nature04296

Stem Cells of Aging Donors- Insufficient Capacity to Repair Causes & Progression of Atherosclerosis in the Recipient

Authors
Karra, R; Vemullapalli, S; Dong, C; Herderick, EE; Song, X; Slosek, K; Nevins, JR; West, M; Goldschmidt-Clermont, PJ; Seo, D
MLA Citation
Karra, R, Vemullapalli, S, Dong, C, Herderick, EE, Song, X, Slosek, K, Nevins, JR, West, M, Goldschmidt-Clermont, PJ, and Seo, D. "Stem Cells of Aging Donors- Insufficient Capacity to Repair Causes & Progression of Atherosclerosis in the Recipient." Journal of the American Society of Nephrology 17 (2006): 317-322. (Academic Article)
Source
manual
Published In
Journal of the American Society of Nephrology
Volume
17
Publish Date
2006
Start Page
317
End Page
322

Sparse statistical modelling in gene expression genomics

Authors
Lucas, JE; Carvalho, CM; Wang, Q; Bild, AH; Nevins, JR; West, M
MLA Citation
Lucas, JE, Carvalho, CM, Wang, Q, Bild, AH, Nevins, JR, and West, M. "Sparse statistical modelling in gene expression genomics." Bayesian Inference for Gene Expression and Proteomics. Ed. KA Do, P Mueller, and M Vannucci. Cambridge University Press, 2006. 155-176.
Source
manual
Publish Date
2006
Start Page
155
End Page
176

Data augmentation in multi-way contingency tables with fixed marginal totals

We describe and illustrate approaches to data augmentation in multi-way contingency tables for which partial information, in the form of subsets of marginal totals, is available. In such problems, interest lies in questions of inference about the parameters of models underlying the table together with imputation for the individual cell entries. We discuss questions of structure related to the implications for inference on cell counts arising from assumptions about log-linear model forms, and a class of simple and useful prior distributions on the parameters of log-linear models. We then discuss "local move" and "global move" Metropolis-Hastings simulation methods for exploring the posterior distributions for parameters and cell counts, focusing particularly on higher-dimensional problems. As a by-product, we note potential uses of the "global move" approach for inference about numbers of tables consistent with a prescribed subset of marginal counts. Illustration and comparison of MCMC approaches is given, and we conclude with discussion of areas for further developments and current open issues. © 2004 Elsevier B.V. All rights reserved.

Authors
Dobra, A; Tebaldi, C; West, M
MLA Citation
Dobra, A, Tebaldi, C, and West, M. "Data augmentation in multi-way contingency tables with fixed marginal totals." Journal of Statistical Planning and Inference 136.2 (2006): 355-372.
Source
scival
Published In
Journal of Statistical Planning and Inference
Volume
136
Issue
2
Publish Date
2006
Start Page
355
End Page
372
DOI
10.1016/j.jspi.2004.07.002

Multiscale and hidden resolution time series models

Authors
Ferreira, MAR; West, M; Lee, HKH; Higdon, DM
MLA Citation
Ferreira, MAR, West, M, Lee, HKH, and Higdon, DM. "Multiscale and hidden resolution time series models." Bayesian Analysis 2 (2006): 294-314. (Academic Article)
Source
manual
Published In
Bayesian Analysis
Volume
2
Publish Date
2006
Start Page
294
End Page
314

High-dimensional regression in cancer genomics

Authors
Hans, C; West, M
MLA Citation
Hans, C, and West, M. "High-dimensional regression in cancer genomics." Bulletin of the International Society for Bayesian Analysis 13 (2006): 2-3. (Academic Article)
Source
manual
Published In
Bulletin of the International Society for Bayesian Analysis
Volume
13
Publish Date
2006
Start Page
2
End Page
3

Multi-scale and hidden resolution time series models

We introduce a class of multi-scale models for time series. The novel framework couples standard linear models at different levels of resolution via stochastic links across scales. Jeffrey's rule of conditioning is used to revise the implied distributions and ensure that the probability distributions at the different levels are strictly compatible. This results in a new class of models for time series with three key characteristics: this class exhibits a variety of autocorrelation structures based on a parsimonious parameterization, it has the ability to combine information across levels of resolution, and it also has the capacity to emulate long memory processes. The potential applications of such multi-scale models include problems in which it is of interest to develop consistent stochastic models across time-scales and levels of resolution, in order to coherently combine and integrate information arising at different levels of resolution. Bayesian estimation based on MCMC analysis and forecasting based on simulation are developed. One application to the analysis of the flow of a river illustrates the new class of models and its utility. © 2006 International Society for Bayesian Analysis.

Authors
Ferreira, MAR; West, M; Lee, HKH; Higdon, DM
MLA Citation
Ferreira, MAR, West, M, Lee, HKH, and Higdon, DM. "Multi-scale and hidden resolution time series models." Bayesian Analysis 1.4 (2006): 947-968.
Source
scival
Published In
Bayesian Analysis
Volume
1
Issue
4
Publish Date
2006
Start Page
947
End Page
968
DOI
10.1214/06-BA131

Molecular evidence for arterial repair in atherosclerosis.

Atherosclerosis is a chronic inflammatory process and progresses through characteristic morphologic stages. We have shown previously that chronically injecting bone-marrow-derived vascular progenitor cells can effect arterial repair. This repair capacity depends on the age of the injected marrow cells, suggesting a progressive decline in progenitor cell function. We hypothesized that the progression of atherosclerosis coincides with the deteriorating repair capacity of the bone marrow. Here, we ascribe patterns of gene expression that accurately and reproducibly identify specific disease states in murine atherosclerosis. We then use these expression patterns to determine the point in the disease process at which the repair of arteries by competent bone marrow cells ceases to be efficient. We show that the loss of the molecular signature for competent repair is concurrent with the initiation of atherosclerotic lesions. This work provides a previously unreported comprehensive molecular data set using broad-based analysis that links the loss of successful repair with the progression of a chronic illness.

Authors
Karra, R; Vemullapalli, S; Dong, C; Herderick, EE; Song, X; Slosek, K; Nevins, JR; West, M; Goldschmidt-Clermont, PJ; Seo, D
MLA Citation
Karra, R, Vemullapalli, S, Dong, C, Herderick, EE, Song, X, Slosek, K, Nevins, JR, West, M, Goldschmidt-Clermont, PJ, and Seo, D. "Molecular evidence for arterial repair in atherosclerosis." Proc Natl Acad Sci U S A 102.46 (November 15, 2005): 16789-16794.
PMID
16275914
Source
pubmed
Published In
Proceedings of the National Academy of Sciences of USA
Volume
102
Issue
46
Publish Date
2005
Start Page
16789
End Page
16794
DOI
10.1073/pnas.0507718102

Distinctions in the specificity of E2F function revealed by gene expression signatures.

The E2F family of transcription factors provides essential activities for coordinating the control of cellular proliferation and cell fate. Both E2F1 and E2F3 proteins have been shown to be particularly important for cell proliferation, whereas the E2F1 protein has the capacity to promote apoptosis. To explore the basis for this specificity of function, we used DNA microarray analysis to probe for the distinctions in the two E2F activities. Gene expression profiles that distinguish either E2F1- or E2F3-expressing cells from quiescent cells are enriched in genes encoding cell cycle and DNA replication activities, consistent with many past studies. E2F1 profile is also enriched in genes known to function in apoptosis. We also identified patterns of gene expression that specifically differentiate the activity of E2F1 and E2F3; this profile is enriched in genes known to function in mitosis. The specificity of E2F function has been attributed to protein interactions mediated by the marked box domain, and we now show that chimeric E2F proteins generate expression signatures that reflect the origin of the marked box, thus linking the biochemical mechanism for specificity of function with specificity of gene activation.

Authors
Black, EP; Hallstrom, T; Dressman, HK; West, M; Nevins, JR
MLA Citation
Black, EP, Hallstrom, T, Dressman, HK, West, M, and Nevins, JR. "Distinctions in the specificity of E2F function revealed by gene expression signatures." Proc Natl Acad Sci U S A 102.44 (November 1, 2005): 15948-15953.
PMID
16249342
Source
pubmed
Published In
Proceedings of the National Academy of Sciences of USA
Volume
102
Issue
44
Publish Date
2005
Start Page
15948
End Page
15953
DOI
10.1073/pnas.0504300102

Oncogenic pathway signatures in human cancers as a guide to targeted therapies.

Authors
Bild, AH; Yao, G; Chang, JT; Wang, QL; Potti, A; Harpole, D; Lancaster, J; Berchuck, A; Olson, JA; Marks, J; Dressman, HK; West, M; Nevins, JR
MLA Citation
Bild, AH, Yao, G, Chang, JT, Wang, QL, Potti, A, Harpole, D, Lancaster, J, Berchuck, A, Olson, JA, Marks, J, Dressman, HK, West, M, and Nevins, JR. "Oncogenic pathway signatures in human cancers as a guide to targeted therapies." November 2005.
Source
wos-lite
Published In
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
Volume
14
Issue
11
Publish Date
2005
Start Page
2743S
End Page
2743S

DIG--a system for gene annotation and functional discovery.

SUMMARY: We describe a database and information discovery system named DIG (Duke Integrated Genomics) designed to facilitate the process of gene annotation and the discovery of functional context. The DIG system collects and organizes gene annotation and functional information, and includes tools that support an understanding of genes in a functional context by providing a framework for integrating and visualizing gene expression, protein interaction and literature-based interaction networks.

Authors
Delong, M; Yao, G; Wang, Q; Dobra, A; Black, EP; Chang, JT; Bild, A; West, M; Nevins, JR; Dressman, H
MLA Citation
Delong, M, Yao, G, Wang, Q, Dobra, A, Black, EP, Chang, JT, Bild, A, West, M, Nevins, JR, and Dressman, H. "DIG--a system for gene annotation and functional discovery." Bioinformatics (Oxford, England) 21.13 (July 2005): 2957-2959.
PMID
15870167
Source
epmc
Published In
Bioinformatics
Volume
21
Issue
13
Publish Date
2005
Start Page
2957
End Page
2959
DOI
10.1093/bioinformatics/bti467

Gene expression profiling links invasion-related genes to poor survival in older glioblastoma patients

Authors
Rich, J; Hans, C; Jones, B; McLendon, R; Rasheed, B; Dobra, A; Dressman, H; Bigner, D; Nevins, J; West, M
MLA Citation
Rich, J, Hans, C, Jones, B, McLendon, R, Rasheed, B, Dobra, A, Dressman, H, Bigner, D, Nevins, J, and West, M. "Gene expression profiling links invasion-related genes to poor survival in older glioblastoma patients." July 2005.
Source
wos-lite
Published In
Neuro-Oncology
Volume
7
Issue
3
Publish Date
2005
Start Page
293
End Page
293

Gene expression signatures for prognosis in NSCLC, coupled with signatures of oncogenic pathway deregulation, provide a novel approach for selection of molecular targets.

Authors
Petersen, RP; Bild, A; Dressman, H; Joshi, MBM; Conlon, DH; West, M; Nevins, JR; Harpole, DH
MLA Citation
Petersen, RP, Bild, A, Dressman, H, Joshi, MBM, Conlon, DH, West, M, Nevins, JR, and Harpole, DH. "Gene expression signatures for prognosis in NSCLC, coupled with signatures of oncogenic pathway deregulation, provide a novel approach for selection of molecular targets." June 1, 2005.
Source
wos-lite
Published In
Journal of Clinical Oncology
Volume
23
Issue
16
Publish Date
2005
Start Page
626S
End Page
626S

Gene expression signatures for prognosis in NSCLC, coupled with signatures of oncogenic pathway deregulation, provide a novel approach for selection of molecular targets.

7020 Background: Gene microarray analysis can identify signatures that reflect unique aspects of individual tumors and provide precise prognostic information. Previously, we identified gene expression signatures reflecting the deregulation of oncogenic signaling pathways. In this study, we coupled gene expression data with the ability to identify the state of critical regulatory pathways within an individual tumor to determine prognosis.We prepared RNA from 101 stage I non-small cell lung cancer (NSCLC) tumor samples (51 squamous, 50 adenocarcinomas) for gene expression analysis with the Affymetrix U133 GeneChip. Each group consisted of 25 patients who died within 2 years of resection and 25 patients with a >5year survival. We developed predictive models that accurately distinguished patients with good vs. poor prognosis. We validated the model with a leave-one-out cross validation, with distinct training and validation sample sets. These data were used to predict the status of Ras, Src, β-cat, E2F & Myc pathways and then analyzed by hierarchical clustering to identify patterns of pathway deregulation. Results were expressed as a probability of pathway activation and Kaplan-Meier survival analysis was performed stratifying for pathway status.The predictive model had 80% accuracy in distinguishing patients with respect to survival. Kaplan-Meier analysis revealed that patient subgroups defined by distinct patterns of pathway deregulation exhibited statistically significant differences in disease-free survival. Tumors with deregulated Ras and Myc pathways had much worse prognosis than those with only deregulated Ras (69% vs 20% 2-yr survival, p<0.05).The capacity to stratify NSCLC patients according to individual risks using genomic-based prognostic tools provides opportunity for personalized treatment decisions. The use of gene expression data to predict the status of oncogenic signaling pathways provides an opportunity to better characterize the oncogenic process, and may provide a path to selecting targeted therapeutics. Investigations are underway for EGFR, HER2-neu and VEGF pathways. No significant financial relationships to disclose.

Authors
Petersen, RP; Bild, A; Dressman, H; Joshi, MM; Conlon, DH; West, M; Nevins, JR; Harpole, DH
MLA Citation
Petersen, RP, Bild, A, Dressman, H, Joshi, MM, Conlon, DH, West, M, Nevins, JR, and Harpole, DH. "Gene expression signatures for prognosis in NSCLC, coupled with signatures of oncogenic pathway deregulation, provide a novel approach for selection of molecular targets." Journal of clinical oncology : official journal of the American Society of Clinical Oncology 23.16_suppl (June 2005): 7020-.
PMID
27944461
Source
epmc
Published In
Journal of Clinical Oncology
Volume
23
Issue
16_suppl
Publish Date
2005
Start Page
7020

Gene expression profiling and genetic markers in glioblastoma survival.

Despite the strikingly grave prognosis for older patients with glioblastomas, significant variability in patient outcome is experienced. To explore the potential for developing improved prognostic capabilities based on the elucidation of potential biological relationships, we did analyses of genes commonly mutated, amplified, or deleted in glioblastomas and DNA microarray gene expression data from tumors of glioblastoma patients of age >50 for whom survival is known. No prognostic significance was associated with genetic changes in epidermal growth factor receptor (amplified in 17 of 41 patients), TP53 (mutated in 11 of 41 patients), p16INK4A (deleted in 15 of 33 patients), or phosphatase and tensin homologue (mutated in 15 of 41 patients). Statistical analysis of the gene expression data in connection with survival involved exploration of regression models on small subsets of genes, based on computational search over multiple regression models with cross-validation to assess predictive validity. The analysis generated a set of regression models that, when weighted and combined according to posterior probabilities implied by the statistical analysis, identify patterns in expression of a small subset of genes that are associated with survival and have value in assessing survival risks. The dominant genes across such multiple regression models involve three key genes-SPARC (Osteonectin), Doublecortex, and Semaphorin3B-which play key roles in cellular migration processes. Additional analysis, based on statistical graphical association models constructed using similar computational analysis methods, reveals other genes which support the view that multiple mediators of tumor invasion may be important prognostic factor in glioblastomas in older patients.

Authors
Rich, JN; Hans, C; Jones, B; Iversen, ES; McLendon, RE; Rasheed, BKA; Dobra, A; Dressman, HK; Bigner, DD; Nevins, JR; West, M
MLA Citation
Rich, JN, Hans, C, Jones, B, Iversen, ES, McLendon, RE, Rasheed, BKA, Dobra, A, Dressman, HK, Bigner, DD, Nevins, JR, and West, M. "Gene expression profiling and genetic markers in glioblastoma survival." Cancer Res 65.10 (May 15, 2005): 4051-4058.
PMID
15899794
Source
pubmed
Published In
Cancer Research
Volume
65
Issue
10
Publish Date
2005
Start Page
4051
End Page
4058
DOI
10.1158/0008-5472.CAN-04-3936

Patterns of gene expression that characterize long-term survival in advanced stage serous ovarian cancers.

PURPOSE: A better understanding of the underlying biology of invasive serous ovarian cancer is critical for the development of early detection strategies and new therapeutics. The objective of this study was to define gene expression patterns associated with favorable survival. EXPERIMENTAL DESIGN: RNA from 65 serous ovarian cancers was analyzed using Affymetrix U133A microarrays. This included 54 stage III/IV cases (30 short-term survivors who lived <3 years and 24 long-term survivors who lived >7 years) and 11 stage I/II cases. Genes were screened on the basis of their level of and variability in expression, leaving 7,821 for use in developing a predictive model for survival. A composite predictive model was developed that combines Bayesian classification tree and multivariate discriminant models. Leave-one-out cross-validation was used to select and evaluate models. RESULTS: Patterns of genes were identified that distinguish short-term and long-term ovarian cancer survivors. The expression model developed for advanced stage disease classified all 11 early-stage ovarian cancers as long-term survivors. The MAL gene, which has been shown to confer resistance to cancer therapy, was most highly overexpressed in short-term survivors (3-fold compared with long-term survivors, and 29-fold compared with early-stage cases). These results suggest that gene expression patterns underlie differences in outcome, and an examination of the genes that provide this discrimination reveals that many are implicated in processes that define the malignant phenotype. CONCLUSIONS: Differences in survival of advanced ovarian cancers are reflected by distinct patterns of gene expression. This biological distinction is further emphasized by the finding that early-stage cancers share expression patterns with the advanced stage long-term survivors, suggesting a shared favorable biology.

Authors
Berchuck, A; Iversen, ES; Lancaster, JM; Pittman, J; Luo, J; Lee, P; Murphy, S; Dressman, HK; Febbo, PG; West, M; Nevins, JR; Marks, JR
MLA Citation
Berchuck, A, Iversen, ES, Lancaster, JM, Pittman, J, Luo, J, Lee, P, Murphy, S, Dressman, HK, Febbo, PG, West, M, Nevins, JR, and Marks, JR. "Patterns of gene expression that characterize long-term survival in advanced stage serous ovarian cancers." Clin Cancer Res 11.10 (May 15, 2005): 3686-3696.
PMID
15897565
Source
pubmed
Published In
Clinical cancer research : an official journal of the American Association for Cancer Research
Volume
11
Issue
10
Publish Date
2005
Start Page
3686
End Page
3696
DOI
10.1158/1078-0432.CCR-04-2398

Multi-scale random field models

Authors
Ferreira, MAR; Higdon, DM; Lee, HKH; West, M
MLA Citation
Ferreira, MAR, Higdon, DM, Lee, HKH, and West, M. Multi-scale random field models. Institute of Statistics and Decision Sciences, Duke University, 2005.
Source
manual
Publish Date
2005

Physiological and statistical approaches to modelling of synaptic responses

Authors
Patil, P; West, M; Wheal, HV; Turner, DA
MLA Citation
Patil, P, West, M, Wheal, HV, and Turner, DA. "Physiological and statistical approaches to modelling of synaptic responses." Modeling in the Neurosciences: From Biological Systems to Neuromimetic Robots. Ed. GN Reeke, RR Poznanski, KA Lindsay, JR Rosenberg, and O Sporns. Taylor & Francis, CRC Press, 2005. 61-88.
Source
manual
Publish Date
2005
Start Page
61
End Page
88

Bayesian analysis of gene expression profiles links invasion related genes to poor survival in older glioblastoma patients

Authors
Rich, JN; Hans, C; Jones, B; McLendon, RE; Rasheed, A; Dobra, A; Dressman, HK; Bigner, DD; Nevins, JR; West, M
MLA Citation
Rich, JN, Hans, C, Jones, B, McLendon, RE, Rasheed, A, Dobra, A, Dressman, HK, Bigner, DD, Nevins, JR, and West, M. "Bayesian analysis of gene expression profiles links invasion related genes to poor survival in older glioblastoma patients." 2005.
Source
manual
Published In
Proceedings of the American Association for Cancer Research
Volume
2005
Publish Date
2005
Start Page
212
End Page
212

Experiments in stochastic computation for high-dimensional graphical models

We discuss the implementation, development and performance of methods of stochastic computation in Gaussian graphical models. We view these methods from the perspective of high-dimensional model search, with a particular interest in the scalability with dimension of Markov chain Monte Carlo (MCMC) and other stochastic search methods. After reviewing the structure and context of undirected Gaussian graphical models and model uncertainty (covariance selection), we discuss prior specifications, including new priors over models, and then explore a number of examples using various methods of stochastic computation. Traditional MCMC methods are the point of departure for this experimentation; we then develop alternative stochastic search ideas and contrast this new approach with MCMC. Our examples range from low (12-20) to moderate (150) dimension, and combine simple synthetic examples with data analysis from gene expression studies. We conclude with comments about the need and potential for new computational methods in far higher dimensions, including constructive approaches to Gaussian graphical modeling and computation. © Institute of Mathematical Statistics, 2005.

Authors
Jones, B; Carvalho, C; Dobra, A; Hans, C; Carter, C; West, M
MLA Citation
Jones, B, Carvalho, C, Dobra, A, Hans, C, Carter, C, and West, M. "Experiments in stochastic computation for high-dimensional graphical models." Statistical Science 20.4 (2005): 388-400.
Source
scival
Published In
Statistical Science
Volume
20
Issue
4
Publish Date
2005
Start Page
388
End Page
400
DOI
10.1214/088342305000000304

Covariance decomposition in undirected Gaussian graphical models

The covariance between two variables in a multivariate Gaussian distribution is decomposed into a sum of path weights for all paths connecting the two variables in an undirected independence graph. These weights are useful in determining which variables are important in mediating correlation between the two path endpoints. The decomposition arises in undirected Gaussian graphical models and does not require or involve any assumptions of causality. This covariance decomposition is derived using basic linear algebra. The decomposition is feasible for very large numbers of variables if the corresponding precision matrix is sparse, a circumstance that arises in examples such as gene expression studies in functional genomics. Additional computational efficiences are possible when the undirected graph is derived from an acyclic directed graph. © 2005 Biometrika Trust.

Authors
Jones, B; West, M
MLA Citation
Jones, B, and West, M. "Covariance decomposition in undirected Gaussian graphical models." Biometrika 92.4 (2005): 779-786.
Source
scival
Published In
Biometrika
Volume
92
Issue
4
Publish Date
2005
Start Page
779
End Page
786
DOI
10.1093/biomet/92.4.779

Gene expression phenotypes of atherosclerosis.

OBJECTIVE: Fulfilling the promise of personalized medicine by developing individualized diagnostic and therapeutic strategies for atherosclerosis will depend on a detailed understanding of the genes and gene variants that contribute to disease susceptibility and progression. To that end, our group has developed a nonbiased approach congruent with the multigenic concept of complex diseases by identifying gene expression patterns highly associated with disease states in human target tissues. METHODS AND RESULTS: We have analyzed a collection of human aorta samples with varying degrees of atherosclerosis to identify gene expression patterns that predict a disease state or potential susceptibility. We find gene expression signatures that relate to each of these disease measures and are reliable and robust in predicting the classification for new samples with >93% in each analysis. The genes that provide the predictive power include many previously suspected to play a role in atherosclerosis and additional genes without prior association with atherosclerosis. CONCLUSIONS: Hence, we are reporting a novel method for generating a molecular phenotype of disease and then identifying genes whose discriminatory capability strongly implicates their potential roles in human atherosclerosis.

Authors
Seo, D; Wang, T; Dressman, H; Herderick, EE; Iversen, ES; Dong, C; Vata, K; Milano, CA; Rigat, F; Pittman, J; Nevins, JR; West, M; Goldschmidt-Clermont, PJ
MLA Citation
Seo, D, Wang, T, Dressman, H, Herderick, EE, Iversen, ES, Dong, C, Vata, K, Milano, CA, Rigat, F, Pittman, J, Nevins, JR, West, M, and Goldschmidt-Clermont, PJ. "Gene expression phenotypes of atherosclerosis." Arterioscler Thromb Vasc Biol 24.10 (October 2004): 1922-1927.
PMID
15297278
Source
pubmed
Published In
Arteriosclerosis, Thrombosis, and Vascular Biology
Volume
24
Issue
10
Publish Date
2004
Start Page
1922
End Page
1927
DOI
10.1161/01.ATV.0000141358.65242.1f

Bayesian analysis of binary prediction tree models for retrospectively sampled outcomes.

Classification tree models are flexible analysis tools which have the ability to evaluate interactions among predictors as well as generate predictions for responses of interest. We describe Bayesian analysis of a specific class of tree models in which binary response data arise from a retrospective case-control design. We are also particularly interested in problems with potentially very many candidate predictors. This scenario is common in studies concerning gene expression data, which is a key motivating example context. Innovations here include the introduction of tree models that explicitly address and incorporate the retrospective design, and the use of nonparametric Bayesian models involving Dirichlet process priors on the distributions of predictor variables. The model specification influences the generation of trees through Bayes' factor based tests of association that determine significant binary partitions of nodes during a process of forward generation of trees. We describe this constructive process and discuss questions of generating and combining multiple trees via Bayesian model averaging for prediction. Additional discussion of parameter selection and sensitivity is given in the context of an example which concerns prediction of breast tumour status utilizing high-dimensional gene expression data; the example demonstrates the exploratory/explanatory uses of such models as well as their primary utility in prediction. Shortcomings of the approach and comparison with alternative tree modelling algorithms are also discussed, as are issues of modelling and computational extensions.

Authors
Pittman, J; Huang, E; Nevins, J; Wang, Q; West, M
MLA Citation
Pittman, J, Huang, E, Nevins, J, Wang, Q, and West, M. "Bayesian analysis of binary prediction tree models for retrospectively sampled outcomes." Biostatistics 5.4 (October 2004): 587-601.
PMID
15475421
Source
pubmed
Published In
Biostatistics
Volume
5
Issue
4
Publish Date
2004
Start Page
587
End Page
601
DOI
10.1093/biostatistics/kxh011

An overview of genomic data analysis.

Authors
Huang, ES; Nevins, JR; West, M; Kuo, PC
MLA Citation
Huang, ES, Nevins, JR, West, M, and Kuo, PC. "An overview of genomic data analysis." Surgery 136.3 (September 2004): 497-499.
PMID
15349091
Source
pubmed
Published In
Surgery
Volume
136
Issue
3
Publish Date
2004
Start Page
497
End Page
499
DOI
10.1016/j.surg.2004.05.037

Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes.

We describe a comprehensive modeling approach to combining genomic and clinical data for personalized prediction in disease outcome studies. This integrated clinicogenomic modeling framework is based on statistical classification tree models that evaluate the contributions of multiple forms of data, both clinical and genomic, to define interactions of multiple risk factors that associate with the clinical outcome and derive predictions customized to the individual patient level. Gene expression data from DNA microarrays is represented by multiple, summary measures that we term metagenes; each metagene characterizes the dominant common expression pattern within a cluster of genes. A case study of primary breast cancer recurrence demonstrates that models using multiple metagenes combined with traditional clinical risk factors improve prediction accuracy at the individual patient level, delivering predictions more accurate than those made by using a single genomic predictor or clinical data alone. The analysis also highlights issues of communicating uncertainty in prediction and identifies combinations of clinical and genomic risk factors playing predictive roles. Implicated metagenes identify gene subsets with the potential to aid biological interpretation. This framework will extend to incorporate any form of data, including emerging forms of genomic data, and provides a platform for development of models for personalized prognosis.

Authors
Pittman, J; Huang, E; Dressman, H; Horng, C-F; Cheng, SH; Tsou, M-H; Chen, C-M; Bild, A; Iversen, ES; Huang, AT; Nevins, JR; West, M
MLA Citation
Pittman, J, Huang, E, Dressman, H, Horng, C-F, Cheng, SH, Tsou, M-H, Chen, C-M, Bild, A, Iversen, ES, Huang, AT, Nevins, JR, and West, M. "Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes." Proc Natl Acad Sci U S A 101.22 (June 1, 2004): 8431-8436.
PMID
15152076
Source
pubmed
Published In
Proceedings of the National Academy of Sciences of USA
Volume
101
Issue
22
Publish Date
2004
Start Page
8431
End Page
8436
DOI
10.1073/pnas.0401736101

Prediction of optimal versus suboptimal cytoreduction of advanced-stage serous ovarian cancer with the use of microarrays.

OBJECTIVE: The purpose of this study was to define gene expression patterns that are associated with the optimal versus suboptimal debulking of advanced-stage serous ovarian cancers. STUDY DESIGN: RNA from 44 advanced serous ovarian cancers (19 optimal, 25 suboptimal) was evaluated with microarrays that contain >22,000 genes. Genes were screened on the basis of their association with debulking status to obtain the top 120 differentially expressed genes. These genes were then used to develop a predictive model for debulking status, which was subjected to out-of-sample cross validation. RESULTS: We found that patterns of expression of 32 genes can distinguish between optimal and suboptimal debulking with 72.7% predictive accuracy. An analysis of the data that were based on clusters of co-ordinately expressed genes resulted in only a marginal improvement in predictive accuracy (75%). CONCLUSION: These data support the hypothesis that favorable survival that is associated with optimal debulking of advanced ovarian cancers is due to, at least in part, the underlying biologic characteristics of these cancers.

Authors
Berchuck, A; Iversen, ES; Lancaster, JM; Dressman, HK; West, M; Nevins, JR; Marks, JR
MLA Citation
Berchuck, A, Iversen, ES, Lancaster, JM, Dressman, HK, West, M, Nevins, JR, and Marks, JR. "Prediction of optimal versus suboptimal cytoreduction of advanced-stage serous ovarian cancer with the use of microarrays." Am J Obstet Gynecol 190.4 (April 2004): 910-925.
PMID
15118612
Source
pubmed
Published In
American Journal of Obstetrics & Gynecology
Volume
190
Issue
4
Publish Date
2004
Start Page
910
End Page
925
DOI
10.1016/j.ajog.2004.02.005

Prediction of long-term versus short-term survival in advanced stage serous ovarian cancer using expression microarrays.

Authors
Berchuck, A; Iversen, E; Lancaster, JM; Henriott, A; Dressman, H; West, M; Nevins, JR; Marks, JR
MLA Citation
Berchuck, A, Iversen, E, Lancaster, JM, Henriott, A, Dressman, H, West, M, Nevins, JR, and Marks, JR. "Prediction of long-term versus short-term survival in advanced stage serous ovarian cancer using expression microarrays." February 2004.
Source
wos-lite
Published In
Journal of the Society for Gynecologic Investigation (Elsevier)
Volume
11
Issue
2
Publish Date
2004
Start Page
182A
End Page
182A

Bayesian model assessment in factor analysis

Factor analysis has been one of the most powerful and flexible tools for assessment of multivariate dependence and codependence. Loosely speaking, it could be argued that the origin of its success rests in its very exploratory nature, where various kinds of data-relationships amongst the variables at study can be iteratively verified and/or refuted. Bayesian inference in factor analytic models has received renewed attention in recent years, partly due to computational advances but also partly to applied focuses generating factor structures as exemplified by recent work in financial time series modeling. The focus of our current work is on exploring questions of uncertainty about the number of latent factors in a multivariate factor model, combined with methodological and computational issues of model specification and model fitting. We explore reversible jump MCMC methods that build on sets of parallel Gibbs sampling-based analyses to generate suitable empirical proposal distributions and that address the challenging problem of finding efficient proposals in high-dimensional models. Alternative MCMC methods based on bridge sampling are discussed, and these fully Bayesian MCMC approaches are compared with a collection of popular model selection methods in empirical studies. Various additional computational issues are discussed, including situations where prior information is scarce, and the methods are explored in studies of some simulated data sets and an econometric time series example.

Authors
Lopes, HF; West, M
MLA Citation
Lopes, HF, and West, M. "Bayesian model assessment in factor analysis." Statistica Sinica 14.1 (2004): 41-67.
Source
scival
Published In
Statistica Sinica
Volume
14
Issue
1
Publish Date
2004
Start Page
41
End Page
67

Sparse graphical models for exploring gene expression data

We discuss the theoretical structure and constructive methodology for large-scale graphical models, motivated by their potential in evaluating and aiding the exploration of patterns of association in gene expression data. The theoretical discussion covers basic ideas and connections between Gaussian graphical models, dependency networks and specific classes of directed acyclic graphs we refer to as compositional networks. We describe a constructive approach to generating interesting graphical models for very high-dimensional distributions that builds on the relationships between these various stylized graphical representations. Issues of consistency of models and priors across dimension are key. The resulting methods are of value in evaluating patterns of association in large-scale gene expression data with a view to generating biological insights about genes related to a known molecular pathway or set of specified genes. Some initial examples relate to the estrogen receptor pathway in breast cancer, and the Rb-E2F cell proliferation control pathway. © 2004 Elsevier Inc. All rights reserved.

Authors
Dobra, A; Hans, C; Jones, B; Nevins, JR; Yao, G; West, M
MLA Citation
Dobra, A, Hans, C, Jones, B, Nevins, JR, Yao, G, and West, M. "Sparse graphical models for exploring gene expression data." Journal of Multivariate Analysis 90.1 SPEC. ISS. (2004): 196-212.
Source
scival
Published In
Journal of Multivariate Analysis
Volume
90
Issue
1 SPEC. ISS.
Publish Date
2004
Start Page
196
End Page
212
DOI
10.1016/j.jmva.2004.02.009

Monte Carlo Smoothing for Nonlinear Time Series

We develop methods for performing smoothing computations in general state-space models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are presented for generation of sample realizations of historical state sequences. This is carried out in a forward-filtering backward-smoothing procedure that can be viewed as the nonlinear, non-Gaussian counterpart of standard Kalman filter-based simulation smoothers in the linear Gaussian case. Convergence in the mean squared error sense of the smoothed trajectories is proved, showing the validity of our proposed method. The methods are tested in a substantial application for the processing of speech signals represented by a time-varying autoregression and parameterized in terms of time-varying partial correlation coefficients, comparing the results of our algorithm with those from a simple smoother based on the filtered trajectories.

Authors
Godsill, SJ; Doucet, A; West, M
MLA Citation
Godsill, SJ, Doucet, A, and West, M. "Monte Carlo Smoothing for Nonlinear Time Series." Journal of the American Statistical Association 99.465 (2004): 156-168.
Source
scival
Published In
Journal of the American Statistical Association
Volume
99
Issue
465
Publish Date
2004
Start Page
156
End Page
168

Expression profiling: Best practices for Affymetrix array data generation and interpretation for clinical trials

Authors
Hoffman, EP; Awad, T; Spira, A; Palma, J; Webster, T; Wright, G; Buckley, J; Davis, R; Hubbell, E; Jones, W; R Tibshirani, RT; Triche, T; Xiao, W; West, M; Warrington, JA
MLA Citation
Hoffman, EP, Awad, T, Spira, A, Palma, J, Webster, T, Wright, G, Buckley, J, Davis, R, Hubbell, E, Jones, W, R Tibshirani, RT, Triche, T, Xiao, W, West, M, and Warrington, JA. "Expression profiling: Best practices for Affymetrix array data generation and interpretation for clinical trials." Nature Reviews Genetics 5 (2004): 229-237. (Academic Article)
Source
manual
Published In
Nature Reviews Genetics
Volume
5
Publish Date
2004
Start Page
229
End Page
237

Predictive models that combine multiple forms of genomic and clinical data to achieve personalized prediction of outcomes in breast cancer

Authors
Pittman, JL; Tebbit, CL; Black, EP; Dressman, HK; Huang, ES; Olson, JA; Marks, JR; Marcom, PK; Huang, AT; West, M; Nevins, JR
MLA Citation
Pittman, JL, Tebbit, CL, Black, EP, Dressman, HK, Huang, ES, Olson, JA, Marks, JR, Marcom, PK, Huang, AT, West, M, and Nevins, JR. "Predictive models that combine multiple forms of genomic and clinical data to achieve personalized prediction of outcomes in breast cancer." 2004.
Source
wos-lite
Published In
Breast Cancer Research and Treatment
Volume
88
Publish Date
2004
Start Page
S21
End Page
S22

Genomic convergence in the study of human and mouse atherosclerosis

Authors
Seo, D; Karra, R; Wang, T; Dressman, H; West, M; Nevins, J; Goldschmidt, P
MLA Citation
Seo, D, Karra, R, Wang, T, Dressman, H, West, M, Nevins, J, and Goldschmidt, P. "Genomic convergence in the study of human and mouse atherosclerosis." October 28, 2003.
Source
wos-lite
Published In
Circulation
Volume
108
Issue
17
Publish Date
2003
Start Page
282
End Page
282

Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction.

Genomic data, particularly genome-scale measures of gene expression derived from DNA microarray studies, has the potential for adding enormous information to the analysis of biological phenotypes. Perhaps the most successful application of this data has been in the characterization of human cancers, including the ability to predict clinical outcomes. Nevertheless, most analyses have used gene expression profiles to define broad group distinctions, similar to the use of traditional clinical risk factors. As a result, there remains considerable heterogeneity within the broadly defined groups and thus predictions fall short of providing accurate predictions for individual patients. One strategy to resolve this heterogeneity is to make use of multiple gene expression patterns that are more powerful in defining individual characteristics and predicting outcomes than any single gene expression pattern. Statistical tree-based classification systems provide a framework for assessing multiple patterns, that we term metagenes, selecting those that are most capable of resolving the biological heterogeneity. Moreover, this framework provides a mechanism to combine multiple forms of data, both genomic and clinical, to most effectively characterize individual patients and achieve the goal of personalized predictions of clinical outcomes.

Authors
Nevins, JR; Huang, ES; Dressman, H; Pittman, J; Huang, AT; West, M
MLA Citation
Nevins, JR, Huang, ES, Dressman, H, Pittman, J, Huang, AT, and West, M. "Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction." Hum Mol Genet 12 Spec No 2 (October 15, 2003): R153-R157. (Review)
PMID
12928487
Source
pubmed
Published In
Human Molecular Genetics
Volume
12 Spec No 2
Publish Date
2003
Start Page
R153
End Page
R157
DOI
10.1093/hmg/ddg287

Gene expression phenotypes of oncogenic signaling pathways.

Authors
Huang, ES; Black, EP; Dressman, H; West, M; Nevins, JR
MLA Citation
Huang, ES, Black, EP, Dressman, H, West, M, and Nevins, JR. "Gene expression phenotypes of oncogenic signaling pathways." Cell Cycle 2.5 (September 2003): 415-417. (Review)
PMID
12963829
Source
pubmed
Published In
Cell Cycle
Volume
2
Issue
5
Publish Date
2003
Start Page
415
End Page
417

Distinct gene expression phenotypes of cells lacking Rb and Rb family members.

The study of tumor suppressor gene function has been aided by the creation of discrete gene alterations in the mouse. One such example can be seen in the study of tumor suppressor gene function in general and the retinoblastoma (Rb) tumor suppressor in particular. Because the phenotype of a cell is a direct reflection of the gene activity within that cell, a comprehensive analysis of changes in gene activity resulting from the loss of Rb function has the potential to greatly enhance our understanding of Rb biology. We have used DNA microarray analysis to identify gene expression profiles in wild-type and Rb-null mouse embryo fibroblasts, as well as cells lacking other Rb family members, as an approach to developing a more complete understanding of Rb function. In so doing, we have identified gene expression phenotypes that characterize the loss of Rb function, that distinguish a Rb-null cell from a wild-type cell as well as a p107/p130-null cell, and that identify gene regulatory pathways unique to these events. Importantly, the Rb gene expression patterns can identify murine tumors that result from Rb loss of function. We suggest that this is an approach to the eventual understanding of gene regulatory pathways that define a phenotypic state, including those events that lead to tumor development.

Authors
Black, EP; Huang, E; Dressman, H; Rempel, R; Laakso, N; Asa, SL; Ishida, S; West, M; Nevins, JR
MLA Citation
Black, EP, Huang, E, Dressman, H, Rempel, R, Laakso, N, Asa, SL, Ishida, S, West, M, and Nevins, JR. "Distinct gene expression phenotypes of cells lacking Rb and Rb family members." Cancer Res 63.13 (July 1, 2003): 3716-3723.
PMID
12839964
Source
pubmed
Published In
Cancer Research
Volume
63
Issue
13
Publish Date
2003
Start Page
3716
End Page
3723

Gene expression phenotypic models that predict the activity of oncogenic pathways.

High-density DNA microarrays measure expression of large numbers of genes in one assay. The ability to find underlying structure in complex gene expression data sets and rigorously test association of that structure with biological conditions is essential to developing multi-faceted views of the gene activity that defines cellular phenotype. We sought to connect features of gene expression data with biological hypotheses by integrating 'metagene' patterns from DNA microarray experiments in the characterization and prediction of oncogenic phenotypes. We applied these techniques to the analysis of regulatory pathways controlled by the genes HRAS (Harvey rat sarcoma viral oncogene homolog), MYC (myelocytomatosis viral oncogene homolog) and E2F1, E2F2 and E2F3 (encoding E2F transcription factors 1, 2 and 3, respectively). The phenotypic models accurately predict the activity of these pathways in the context of normal cell proliferation. Moreover, the metagene models trained with gene expression patterns evoked by ectopic production of Myc or Ras proteins in primary tissue culture cells properly predict the activity of in vivo tumor models that result from deregulation of the MYC or HRAS pathways. We conclude that these gene expression phenotypes have the potential to characterize the complex genetic alterations that typify the neoplastic state, whether in vitro or in vivo, in a way that truly reflects the complexity of the regulatory pathways that are affected.

Authors
Huang, E; Ishida, S; Pittman, J; Dressman, H; Bild, A; Kloos, M; D'Amico, M; Pestell, RG; West, M; Nevins, JR
MLA Citation
Huang, E, Ishida, S, Pittman, J, Dressman, H, Bild, A, Kloos, M, D'Amico, M, Pestell, RG, West, M, and Nevins, JR. "Gene expression phenotypic models that predict the activity of oncogenic pathways." Nat Genet 34.2 (June 2003): 226-230.
PMID
12754511
Source
pubmed
Published In
Nature Genetics
Volume
34
Issue
2
Publish Date
2003
Start Page
226
End Page
230
DOI
10.1038/ng1167

Gene expression predictors of breast cancer outcomes.

BACKGROUND: Correlation of risk factors with genomic data promises to provide specific treatment for individual patients, and needs interpretation of complex, multivariate patterns in gene expression data, as well as assessment of their ability to improve clinical predictions. We aimed to predict nodal metastatic states and relapse for breast cancer patients. METHODS: We analysed DNA microarray data from samples of primary breast tumours, using non-linear statistical analyses to assess multiple patterns of interactions of groups of genes that have predictive value for the individual patient, with respect to lymph node metastasis and cancer recurrence. FINDINGS: We identified aggregate patterns of gene expression (metagenes) that associate with lymph node status and recurrence, and that are capable of predicting outcomes in individual patients with about 90% accuracy. The metagenes defined distinct groups of genes, suggesting different biological processes underlying these two characteristics of breast cancer. Initial external validation came from similarly accurate predictions of nodal status of a small sample in a distinct population. INTERPRETATION: Multiple aggregate measures of profiles of gene expression define valuable predictive associations with lymph node metastasis and disease recurrence for individual patients. Gene expression data have the potential to aid accurate, individualised, prognosis. Importantly, these data are assessed in terms of precise numerical predictions, with ranges of probabilities of outcome. Precise and statistically valid assessments of risks specific for patients, will ultimately be of most value to clinicians faced with treatment decisions.

Authors
Huang, E; Cheng, SH; Dressman, H; Pittman, J; Tsou, MH; Horng, CF; Bild, A; Iversen, ES; Liao, M; Chen, CM; West, M; Nevins, JR; Huang, AT
MLA Citation
Huang, E, Cheng, SH, Dressman, H, Pittman, J, Tsou, MH, Horng, CF, Bild, A, Iversen, ES, Liao, M, Chen, CM, West, M, Nevins, JR, and Huang, AT. "Gene expression predictors of breast cancer outcomes." Lancet 361.9369 (May 10, 2003): 1590-1596.
PMID
12747878
Source
pubmed
Published In
The Lancet
Volume
361
Issue
9369
Publish Date
2003
Start Page
1590
End Page
1596
DOI
10.1016/S0140-6736(03)13308-9

Multiscale modelling of 1-D permeability fields

Authors
Ferreira, MAR; Bi, Z; West, M; Lee, HKH; Higdon, DM
MLA Citation
Ferreira, MAR, Bi, Z, West, M, Lee, HKH, and Higdon, DM. "Multiscale modelling of 1-D permeability fields." Oxford University Press, 2003.
Source
manual
Published In
Bayesian Statistics 7
Publish Date
2003

Tree models for nonparametric Bayesian inference

Authors
Paddock, S; Ruggeri, F; Lavine, M; West, M
MLA Citation
Paddock, S, Ruggeri, F, Lavine, M, and West, M. "Tree models for nonparametric Bayesian inference." Statistica Sinica 13 (2003): 443-460. (Academic Article)
Source
manual
Published In
Statistica Sinica
Volume
13
Publish Date
2003
Start Page
443
End Page
460

Prediction tree models in clinico-genomics

Authors
Pittman, J; Huang, ES; Nevins, JR; West, M
MLA Citation
Pittman, J, Huang, ES, Nevins, JR, and West, M. "Prediction tree models in clinico-genomics." Bulletin of the International Statistical Institute 54 (2003): 76-76. (Academic Article)
Source
manual
Published In
Bulletin of the International Statistical Institute
Volume
54
Publish Date
2003
Start Page
76
End Page
76

Gene expression profiling for prediction of clinical characteristics of breast cancer.

We have applied techniques of gene expression analysis to the analysis of human breast cancer by identifying metagene models with the capacity to discriminate breast tumors based on estrogen receptor (ER) status as well as the propensity for lymph node metastasis. We assess the utility and validity of these models in predicting status of tumors in cross-validation determinations. The practical value of such approaches relies on the ability not only to assess relative probabilities of clinical outcomes for future samples but also to provide an honest assessment of the uncertainties associated with such predictive classifications, based on the selection of gene subsets for each validation analysis. This latter point is of critical importance to the ability of applying these methodologies to clinical assessment of tumor phenotype. It is also clear from ER predictions that these analyses identify genes known to be involved in ER function but also identify new candidate genes involved in ER function. We believe these gene expression phenotypes have the potential to characterize the complex genetic alterations that typify the neoplastic state in a way that truly reflects the complexity of the regulatory pathways that are affected.

Authors
Huang, E; West, M; Nevins, JR
MLA Citation
Huang, E, West, M, and Nevins, JR. "Gene expression profiling for prediction of clinical characteristics of breast cancer." Recent Prog Horm Res 58 (2003): 55-73. (Review)
PMID
12795414
Source
pubmed
Published In
Recent progress in hormone research
Volume
58
Publish Date
2003
Start Page
55
End Page
73

Erratum: Gene expression phenotypic models that predict the activity of oncogenic pathways (Nature Genetics (2003) 34 (226-230))

Authors
Huang, E; Ishida, S; Pittmann, J; Dressman, H; Bild, A; Kloos, M; D'Amico, M; Pestell, RG; West, M; Nevins, JR
MLA Citation
Huang, E, Ishida, S, Pittmann, J, Dressman, H, Bild, A, Kloos, M, D'Amico, M, Pestell, RG, West, M, and Nevins, JR. "Erratum: Gene expression phenotypic models that predict the activity of oncogenic pathways (Nature Genetics (2003) 34 (226-230))." Nature Genetics 34.4 (2003): 465--.
Source
scival
Published In
Nature Genetics
Volume
34
Issue
4
Publish Date
2003
Start Page
465-
DOI
10.1038/ng0803-465

Randomized Polya tree models for nonparametric Bayesian inference

Like other partition-based models, Polya trees suffer the problem of partition dependence. We develop Randomized Polya Trees to address this limitation. This new framework inherits the structure of Polya trees but "jitters" partition points and as a result smooths discontinuities in predictive distributions. Some of the theoretical aspects of the new framework are developed, followed by discussion of methodological and computational issues arising in implementation. Examples of data analyses and prediction problems are provided to highlight issues of Bayesian inference in this context.

Authors
Paddock, SM; Ruggeri, F; Lavine, M; West, M
MLA Citation
Paddock, SM, Ruggeri, F, Lavine, M, and West, M. "Randomized Polya tree models for nonparametric Bayesian inference." Statistica Sinica 13.2 (2003): 443-460.
Source
scival
Published In
Statistica Sinica
Volume
13
Issue
2
Publish Date
2003
Start Page
443
End Page
460

Bayesian factor regression models in the "Large p, Small n" paradigm

Authors
West, M
MLA Citation
West, M. "Bayesian factor regression models in the "Large p, Small n" paradigm." 2003.
Source
wos-lite
Published In
BAYESIAN STATISTICS 7
Publish Date
2003
Start Page
733
End Page
742

Prediction and uncertainty in the analysis of gene expression profiles.

We have developed a complete statistical model for the analysis of tumor specific gene expression profiles. The approach provides investigators with a global overview on large scale gene expression data, indicating aspects of the data that relate to tumor phenotype, but also summarizing the uncertainties inherent in classification of tumor types. We demonstrate the use of this method in the context of a gene expression profiling study of 27 human breast cancers. The study is aimed at defining molecular characteristics of tumors that reflect estrogen receptor tatus. In addition to good predictive performance with respect to pure classification of the expression profiles, the model also uncovers conflicts in the data with respect to the classification of some of the tumors, highlighting them as critical cases for which additional investigations are appropriate.

Authors
Spang, R; Zuzan, H; West, M; Nevins, J; Blanchette, C; Marks, JR
MLA Citation
Spang, R, Zuzan, H, West, M, Nevins, J, Blanchette, C, and Marks, JR. "Prediction and uncertainty in the analysis of gene expression profiles." In Silico Biol 2.3 (2002): 369-381.
PMID
12542420
Source
pubmed
Published In
In silico biology
Volume
2
Issue
3
Publish Date
2002
Start Page
369
End Page
381

Statistical analyses of freeway traffic flows

This paper concerns the exploration of statistical models for the analysis of observational freeway flow data, and the development of empirical models to capture and predict short-term changes in traffic flow characteristics on sequences of links in a partially detectorized freeway network. A first set of analyses explores regression models for minute-by-minute traffic flows, taking into account time of day, day of the week, and recent upstream detector-based flows. Day-and link-specific random effects are used in a hierarchical statistical modelling framework. A second set of analyses captures day-specific idiosyncrasies in traffic patterns by including parameters that may vary throughout the day. Model fit and short-term predictions of flows are thus improved significantly. A third set of analyses includes recent downstream flows as additional predictors. These further improvements, though marginal in most cases, can be quite radically useful in cases of very marked breakdown of freeway flows on some links. These three modelling stages are described and developed in analyses of observational flow data from a set of links on Interstate Highway 5 (I-5) near Seattle. Copyright © 2002 John Wiley & Sons, Ltd.

Authors
Tebaldi, C; West, M; Karr, AF
MLA Citation
Tebaldi, C, West, M, and Karr, AF. "Statistical analyses of freeway traffic flows." Journal of Forecasting 21.1 (2002): 39-68.
Source
scival
Published In
Journal of Forecasting
Volume
21
Issue
1
Publish Date
2002
Start Page
39
End Page
68
DOI
10.1002/for.804

Markov random field models for high-dimensional parameters in simulations of fluid flow in porous media

We give an approach for using flow information from a system of wells to characterize hydrologic properties of an aquifer. In particular, we consider experiments where an impulse of tracer fluid is injected along with the water at the input wells and its concentration is recorded over time at the uptake wells. We focus on characterizing the spatially varying permeability field, which is a key attribute of the aquifer for determining flow paths and rates for a given flow experiment. As is standard for estimation from such flow data, we use complicated subsurface flow code that simulates the fluid flow through the aquifer for a particular well configuration and aquifer specification, in particular the permeability field over a grid. The solution to this ill-posed problem requires that some regularity conditions be imposed on the permeability field. Typically, this regularity is accomplished by specifying a stationary Gaussian process model for the permeability field. Here we use an intrinsically stationary Markov random field, which compares favorably to Gaussian process models and offers some additional flexibility and computational advantages. Our interest in quantifying uncertainty leads us to take a Bayesian approach, using Markov chain Monte Carlo for exploring the high-dimensional posterior distribution. We demonstrate our approach with several examples. We also note that the methodology is general and is not specific to hydrology applications.

Authors
Lee, HKH; Higdon, DM; Bi, Z; Ferreira, MAR; West, M
MLA Citation
Lee, HKH, Higdon, DM, Bi, Z, Ferreira, MAR, and West, M. "Markov random field models for high-dimensional parameters in simulations of fluid flow in porous media." Technometrics 44.3 (2002): 230-241.
Source
scival
Published In
Technometrics
Volume
44
Issue
3
Publish Date
2002
Start Page
230
End Page
241
DOI
10.1198/004017002188618419

Monte Carlo smoothing with application to audio signal enhancement

We describe methods for applying Monte Carlo filtering and smoothing for estimation of unobserved states in a nonlinear state-space model. By exploiting the statistical structure of the model, we develop a Rao-Blackwellized particle smoother. Due to the lengthy nature of real signals, we suggest processing the data in blocks, and a block-based smoother algorithm is developed for this purpose. All the algorithms suggested are tested with real speech and audio data, and the results are shown and compared with those generated using the generic particle smoother and the extended Kalman filter (EKF). It is found that the proposed Rao-Blackwellized particle smoother improves on the standard particle smoother and the extended Kalman smoother. In addition, the proposed Block-based smoother algorithm enhances the efficiency of the proposed Rao-Blackwellized smoother by significantly reducing the storage capacity required for the particle in, formation.

Authors
Fong, W; Godsill, SJ; Doucet, A; West, M
MLA Citation
Fong, W, Godsill, SJ, Doucet, A, and West, M. "Monte Carlo smoothing with application to audio signal enhancement." IEEE Transactions on Signal Processing 50.2 (2002): 438-449.
Source
scival
Published In
IEEE Transactions on Signal Processing
Volume
50
Issue
2
Publish Date
2002
Start Page
438
End Page
449
DOI
10.1109/78.978397

Predicting the clinical status of human breast cancer by using gene expression profiles.

Prognostic and predictive factors are indispensable tools in the treatment of patients with neoplastic disease. For the most part, such factors rely on a few specific cell surface, histological, or gross pathologic features. Gene expression assays have the potential to supplement what were previously a few distinct features with many thousands of features. We have developed Bayesian regression models that provide predictive capability based on gene expression data derived from DNA microarray analysis of a series of primary breast cancer samples. These patterns have the capacity to discriminate breast tumors on the basis of estrogen receptor status and also on the categorized lymph node status. Importantly, we assess the utility and validity of such models in predicting the status of tumors in crossvalidation determinations. The practical value of such approaches relies on the ability not only to assess relative probabilities of clinical outcomes for future samples but also to provide an honest assessment of the uncertainties associated with such predictive classifications on the basis of the selection of gene subsets for each validation analysis. This latter point is of critical importance in the ability to apply these methodologies to clinical assessment of tumor phenotype.

Authors
West, M; Blanchette, C; Dressman, H; Huang, E; Ishida, S; Spang, R; Zuzan, H; Olson, JA; Marks, JR; Nevins, JR
MLA Citation
West, M, Blanchette, C, Dressman, H, Huang, E, Ishida, S, Spang, R, Zuzan, H, Olson, JA, Marks, JR, and Nevins, JR. "Predicting the clinical status of human breast cancer by using gene expression profiles." Proc Natl Acad Sci U S A 98.20 (September 25, 2001): 11462-11467.
PMID
11562467
Source
pubmed
Published In
Proceedings of the National Academy of Sciences of USA
Volume
98
Issue
20
Publish Date
2001
Start Page
11462
End Page
11467
DOI
10.1073/pnas.201162998

Role for E2F in control of both DNA replication and mitotic functions as revealed from DNA microarray analysis.

We have used high-density DNA microarrays to provide an analysis of gene regulation during the mammalian cell cycle and the role of E2F in this process. Cell cycle analysis was facilitated by a combined examination of gene control in serum-stimulated fibroblasts and cells synchronized at G(1)/S by hydroxyurea block that were then released to proceed through the cell cycle. The latter approach (G(1)/S synchronization) is critical for rigorously maintaining cell synchrony for unambiguous analysis of gene regulation in later stages of the cell cycle. Analysis of these samples identified seven distinct clusters of genes that exhibit unique patterns of expression. Genes tend to cluster within these groups based on common function and the time during the cell cycle that the activity is required. Placed in this context, the analysis of genes induced by E2F proteins identified genes or expressed sequence tags not previously described as regulated by E2F proteins; surprisingly, many of these encode proteins known to function during mitosis. A comparison of the E2F-induced genes with the patterns of cell growth-regulated gene expression revealed that virtually all of the E2F-induced genes are found in only two of the cell cycle clusters; one group was regulated at G(1)/S, and the second group, which included the mitotic activities, was regulated at G(2). The activation of the G(2) genes suggests a broader role for E2F in the control of both DNA replication and mitotic activities.

Authors
Ishida, S; Huang, E; Zuzan, H; Spang, R; Leone, G; West, M; Nevins, JR
MLA Citation
Ishida, S, Huang, E, Zuzan, H, Spang, R, Leone, G, West, M, and Nevins, JR. "Role for E2F in control of both DNA replication and mitotic functions as revealed from DNA microarray analysis." Mol Cell Biol 21.14 (July 2001): 4684-4699.
PMID
11416145
Source
pubmed
Published In
Molecular and Cellular Biology
Volume
21
Issue
14
Publish Date
2001
Start Page
4684
End Page
4699
DOI
10.1128/MCB.21.14.4684-4699.2001

Maximum a posteriori sequence estimation using Monte Carlo particle filters

Authors
Godsill, S; Doucet, A; West, M
MLA Citation
Godsill, S, Doucet, A, and West, M. "Maximum a posteriori sequence estimation using Monte Carlo particle filters." ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 53.1 (March 2001): 82-96.
Source
wos-lite
Published In
Annals of the Institute of Statistical Mathematics
Volume
53
Issue
1
Publish Date
2001
Start Page
82
End Page
96
DOI
10.1023/A:1017968404964

Case Studies in Bayesian Statistics V

Authors
West, M
MLA Citation
West, M. "Case Studies in Bayesian Statistics V." Springer-Verlag, New York, 2001.
Source
manual
Publish Date
2001

Case Studies in Bayesian Statistics V

Authors
West, M
MLA Citation
West, M. "Case Studies in Bayesian Statistics V." Springer-Verlag, New York, 2001.
Source
manual
Publish Date
2001

Combined parameter and state estimation in simulation-based filtering

Authors
Liu, J; West, M
MLA Citation
Liu, J, and West, M. "Combined parameter and state estimation in simulation-based filtering." Sequential Monte Carlo Methods in Practice. Ed. A Doucet, JFGD Freitas, and NJ Gordon. New York: Springer-Verlag, 2001. 197-217.
Source
manual
Publish Date
2001
Start Page
197
End Page
217

Prediction and uncertainty in the analysis of gene expression profiles

Authors
Spang, R; Zuzan, H; West, M; Nevins, JR; Blanchette, C; Marks, JR
MLA Citation
Spang, R, Zuzan, H, West, M, Nevins, JR, Blanchette, C, and Marks, JR. "Prediction and uncertainty in the analysis of gene expression profiles." In Silico Biology 2 (2001): 0033-0033. (Academic Article)
Source
manual
Published In
In Silico Biology
Volume
2
Publish Date
2001
Start Page
0033
End Page
0033

Multi-channel EEG analyses via dynamic regression models with time-varying lag/lead structure

Authors
Prado, R; West, M; Krystal, AD
MLA Citation
Prado, R, West, M, and Krystal, AD. "Multi-channel EEG analyses via dynamic regression models with time-varying lag/lead structure." Journal of the Royal Statistical Society (Ser. C) 50 (2001): 95-110. (Academic Article)
Source
manual
Published In
Journal of the Royal Statistical Society (Ser. C)
Volume
50
Publish Date
2001
Start Page
95
End Page
110

Bayesian time-varying autoregressions: Theory, methods and applications

Authors
Prado, R; Huerta, G; West, M
MLA Citation
Prado, R, Huerta, G, and West, M. "Bayesian time-varying autoregressions: Theory, methods and applications." Resenhas 4 (2001): 405-422. (Academic Article)
Source
manual
Published In
Resenhas
Volume
4
Publish Date
2001
Start Page
405
End Page
422

Multichannel electroencephalographic analyses via dynamic regression models with time-varying lag-lead structure

Multiple time series of scalp electrical potential activity are generated routinely in electroencephalographic (EEG) studies. Such recordings provide important non-invasive data about brain function in human neuropsychiatric disorders. Analyses of EEG traces aim to isolate characteristics of their spatiotemporal dynamics that may be useful in diagnosis, or may improve the understanding of the underlying neurophysiology or may improve treatment through identifying predictors and indicators of clinical outcomes. We discuss the development and application of non-stationary time series models for multiple EEG series generated from individual subjects in a clinical neuropsychiatric setting. The subjects are depressed patients experiencing generalized tonic-clonic seizures elicited by electroconvulsive therapy (ECT) as antidepressant treatment. Two varieties of models - dynamic latent factor models and dynamic regression models - are introduced and studied. We discuss model motivation and form, and aspects of statistical analysis including parameter identifiability, posterior inference and implementation of these models via Markov chain Monte Carlo techniques. In an application to the analysis of a typical set of 19 EEG series recorded during an ECT seizure at different locations over a patient's scalp, these models reveal time-varying features across the series that are strongly related to the placement of the electrodes. We illustrate various model outputs, the exploration of such time-varying spatial structure and its relevance in the ECT study, and in basic EEG research in general.

Authors
Prado, R; West, M; Krystal, AD
MLA Citation
Prado, R, West, M, and Krystal, AD. "Multichannel electroencephalographic analyses via dynamic regression models with time-varying lag-lead structure." Journal of the Royal Statistical Society. Series C: Applied Statistics 50.1 (2001): 95-109.
Source
scival
Published In
Journal of the Royal Statistical Society. Series C: Applied Statistics
Volume
50
Issue
1
Publish Date
2001
Start Page
95
End Page
109

EEG effects of ECT: implications for rTMS.

Electroconvulsive therapy (ECT) involves the use of electrical stimulation to elicit a series of generalized tonic-clonic seizures for therapeutic purposes and is the most effective treatment known for major depression. These treatments have significant neurophysiologic effects, many of which are manifest in the electroencephalogram (EEG). The relationship between EEG data and the response to ECT has been studied since the 1940s, but for many years no consistent correlates were found. Recent studies indicate that a number of specific EEG features recorded during the induced seizures (ictal EEG) as well as before and after a course of treatment (interictal EEG) are related to both the therapeutic efficacy and cognitive side effects. Similar to ECT, repetitive transcranial magnetic stimulation (rTMS), which involves focal electromagnetic stimulation of cortical neurons, has also been studied as an antidepressant therapy and also appears to have neurophysiologic effects, although these have not been as fully investigated as is the case with ECT. Given the similarity of these treatments, it is natural to consider whether advances in understanding the electrophysiologic correlates of the ECT response might have implications for rTMS. The present article reviews the literature on the EEG effects of ECT and discusses the implications in terms of the likely efficacy and side effects associated with rTMS in specific anatomic locations, the potential for producing an antidepressant response with rTMS without eliciting seizure activity, eliciting focal seizures with rTMS, and the possibility of using rTMS to focally modulate seizure induction and spread with ECT to optimize treatment.

Authors
Krystal, AD; West, M; Prado, R; Greenside, H; Zoldi, S; Weiner, RD
MLA Citation
Krystal, AD, West, M, Prado, R, Greenside, H, Zoldi, S, and Weiner, RD. "EEG effects of ECT: implications for rTMS." Depress Anxiety 12.3 (2000): 157-165. (Review)
PMID
11126190
Source
pubmed
Published In
Depression and Anxiety
Volume
12
Issue
3
Publish Date
2000
Start Page
157
End Page
165
DOI
10.1002/1520-6394(2000)12:3<157::AID-DA7>3.0.CO;2-R

Profiling mental health provider trends in health care delivery systems

Authors
Burgess, J; Lourdes, V; West, M
MLA Citation
Burgess, J, Lourdes, V, and West, M. "Profiling mental health provider trends in health care delivery systems." Health Services & Outcomes Research Methodology 100 (2000): 253-276. (Academic Article)
Source
manual
Published In
Health Services & Outcomes Research Methodology
Volume
100
Publish Date
2000
Start Page
253
End Page
276

Bayesian dynamic factor models and portfolio allocation

We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and explored in a study of the dynamic factor structure of daily spot exchange rates for a selection of international currencies. The models are direct generalizations of univariate stochastic volatility models and represent specific varieties of models recently discussed in the growing multivariate stochastic volatility literature. We discuss model fitting based on retrospective data and sequential analysis for forward filtering and short-term forecasting. Analyses are compared with results from the much simpler method of dynamic variance-matrix discounting that, for over a decade, has been a standard approach in applied financial econometrics. We study these models in analysis, forecasting, and sequential portfolio allocation for a selected set of international exchange-rate-return time series. Our goals are to understand a range of modeling questions arising in using these factor models and to explore empirical performance in portfolio construction relative to discount approaches. We report on our experiences and conclude with comments about the practical utility of structured factor models and on future potential model extensions.

Authors
Aguilar, O; West, M
MLA Citation
Aguilar, O, and West, M. "Bayesian dynamic factor models and portfolio allocation." Journal of Business and Economic Statistics 18.3 (2000): 338-357.
Source
scival
Published In
Journal of Business and Economic Statistics
Volume
18
Issue
3
Publish Date
2000
Start Page
338
End Page
357

Monte Carlo filtering and smoothing with application to time-varying spectral estimation

We develop methods for performing filtering and smoothing in non-linear non-Gaussian dynamical models. The methods rely on a particle cloud representation of the filtering distribution which evolves through time using importance sampling and resampling ideas. In particular, novel techniques are presented for generation of random realisations from the joint smoothing distribution and for MAP estimation of the state sequence. Realisations of the smoothing distribution are generated in a forward-backward procedure, while the MAP estimation procedure can be performed in a single forward pass of the Viterbi algorithm applied to a discretised version of the state space. An application to spectral estimation for time-varying autoregressions is described.

Authors
Doucet, A; Godsill, SJ; West, M
MLA Citation
Doucet, A, Godsill, SJ, and West, M. "Monte Carlo filtering and smoothing with application to time-varying spectral estimation." ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2 (2000): 701-704.
Source
scival
Published In
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume
2
Publish Date
2000
Start Page
701
End Page
704

Priors and component structures in autoregressive time series models (vol 61, pg 881, 1999)

Authors
Huerta, G; West, M
MLA Citation
Huerta, G, and West, M. "Priors and component structures in autoregressive time series models (vol 61, pg 881, 1999)." JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 62 (2000): 429-429.
Source
wos-lite
Published In
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Volume
62
Publish Date
2000
Start Page
429
End Page
429
DOI
10.1111/1467-9868.00241

New methods of time series analysis of non-stationary EEG data: eigenstructure decompositions of time varying autoregressions.

OBJECTIVE: Those who analyze EEG data require quantitative techniques that can be validly applied to time series exhibiting ranges of non-stationary behavior. Our objective is to introduce a new analysis technique based on formal non-stationary time series models. This novel method provides a decomposition of the time series into a set of 'latent' components with time-varying frequency content. The identification of these components can lead to practical insights and quantitative comparisons of changes in frequency structure over time in EEG time series. METHODS: The technique begins with the development of time-varying autoregressive models of the EEG time series. Such models have been previously used in EEG analysis but we extend their utility by the introduction of eigenstructure decomposition methods. We review the basis and implementation of this method and report on the analysis of two channel EEG data recorded during 3 generalized tonic-clonic seizures induced in an individual as part of a course of electroconvulsive therapy for major depression. RESULTS: This technique identified EEG patterns consistent with prior reports. In addition, it quantified a decrease in dominant frequency content over the seizures and suggested for the first time that this decrease is continuous across the end of the seizures. The analysis also suggested that the seizure EEG may be best modeled by the combination of multiple processes, whereas post-ictally there appears to be one dominant process. There was also preliminary evidence that these features may differ as a function of ECT therapeutic effectiveness. CONCLUSIONS: Eigenanalysis of time-varying autoregressive models has promise for improving the analysis of EEG time series.

Authors
Krystal, AD; Prado, R; West, M
MLA Citation
Krystal, AD, Prado, R, and West, M. "New methods of time series analysis of non-stationary EEG data: eigenstructure decompositions of time varying autoregressions." Clin Neurophysiol 110.12 (December 1999): 2197-2206.
PMID
10616127
Source
pubmed
Published In
Clinical Neurophysiology
Volume
110
Issue
12
Publish Date
1999
Start Page
2197
End Page
2206

Analysis of hospital quality monitors using hierarchical time series models

Authors
Aguilar, O; West, M
MLA Citation
Aguilar, O, and West, M. "Analysis of hospital quality monitors using hierarchical time series models." Springer Verlag, New York, 1999.
Source
manual
Published In
Case Studies in Bayesian Statistics, Vol. 4
Publish Date
1999
Start Page
287
End Page
302

Analysis of hospital quality monitors using hierarchical time series models

Authors
Aguilar, O; West, M
MLA Citation
Aguilar, O, and West, M. "Analysis of hospital quality monitors using hierarchical time series models." Springer Verlag, New York, 1999.
Source
manual
Published In
Case Studies in Bayesian Statistics, Vol. 4
Publish Date
1999
Start Page
287
End Page
302

The spatiotemporal dynamics of generalized tonic-clonic seizure EEG data: Relevance to the climinal practice of electroconvulsive therapy

Authors
Krystal, AD; Zoldi, S; Prado, R; Greenside, HS; West, M
MLA Citation
Krystal, AD, Zoldi, S, Prado, R, Greenside, HS, and West, M. "The spatiotemporal dynamics of generalized tonic-clonic seizure EEG data: Relevance to the climinal practice of electroconvulsive therapy." Nonlinear Dynamics and Brain Functioning. Ed. N Pradhan, PE Rapp, and R Sreenivasan. New York: Novascience, 1999.
Source
manual
Publish Date
1999

Statistical inference of synaptic responses: Physiological and statistical approaches

Authors
Turner, DA; Chen, Y; Wheal, HV; West, M
MLA Citation
Turner, DA, Chen, Y, Wheal, HV, and West, M. "Statistical inference of synaptic responses: Physiological and statistical approaches." Modelling in the Neurosciences: From Ion Channels to Neural Networks. Ed. RR Poznanski. London, Gordon & Breach Publishers, 1999. 39-78.
Source
manual
Publish Date
1999
Start Page
39
End Page
78

Bayesian inference on periodicities and component spectral structure in time series

We detail and illustrate time series analysis and spectral inference in autoregressive models with a focus on the underlying latent structure and time series decompositions. A novel class of priors on parameters of latent components leads to a new class of smoothness priors on autoregressive coefficients, provides for formal inference on model order, including very high order models, and leads to the incorporation of uncertainty about model order into summary inferences. The class of prior models also allows for subsets of unit roots, and hence leads to inference on sustained though stochastically time-varying periodicities in time series. Applications to analysis of the frequency composition of time series, in both time and spectral domains, is illustrated in a study of a time series from astronomy. This analysis demonstrates the impact and utility of the new class of priors in addressing model order uncertainty and in allowing for unit root structure. Time-domain decomposition of a time series into estimated latent components provides an important alternative view of the component spectral characteristics of a series. In addition, our data analysis illustrates the utility of the smoothness prior and allowance for unit root structure in inference about spectral densities. In particular, the framework overcomes supposed problems in spectral estimation with autoregressive models using more traditional model-fitting methods.

Authors
Huerta, G; West, M
MLA Citation
Huerta, G, and West, M. "Bayesian inference on periodicities and component spectral structure in time series." Journal of Time Series Analysis 20.4 (1999): 401-416.
Source
scival
Published In
Journal of Time Series Analysis
Volume
20
Issue
4
Publish Date
1999
Start Page
401
End Page
416

Evaluation and Comparison of EEG Traces: Latent Structure in Nonstationary Time Series

We explore and illustrate the use of time series decomposition methods for evaluating and comparing latent structure in nonstationary electroencephalographic (EEG) traces obtained from depressed patients during brain seizures induced as part of electroconvulsive therapy (ECT). Analysis of the patterns of change over time in the frequency structure of such EEG data provides insight into the neurophysiological mechanisms of action of this effective but poorly understood antidepressant treatment, and allows clinicians to modify ECT treatments to optimize therapeutic benefits while minimizing associated side effects. Our work has introduced new methods of time-frequency analysis of EEG series that identify the complete pattern of time evolution of frequency structure over the course of a seizure, and usefully assist in these scientific and clinical studies. New methods of decomposition of flexible dynamic models provide time domain decompositions of individual EEG series into collections of latent components in different frequency bands. This allows us to explore ECT seizure characteristics via inferences on the time-varying parameters that characterize these latent components, and to relate differences in such characteristics across seizures to differences in the therapeutic effectiveness and cognitive side effects of those seizures. This article discusses the scientific context and problems, development of nonstationary time series models and new methods of decomposition to explore time-frequency structure, and aspects of model fitting and analysis. We include applied studies on two datasets from recent clinical ECT studies. One is an initial illustrative analysis of a single EEG trace, the second compares the EEG data recorded during two types of ECT treatment that differ in therapeutic effectiveness and cognitive side effects. The uses of these models and time series decomposition methods in extracting and contrasting key features of the seizure underlying the EEG signals are highlighted. Through the use of these models we have quantified, for the first time, decreases in the dominant frequencies of low-frequency EEG components during ECT seizures. We have also identified preliminary evidence that such decreases are enhanced under the more effective ECTs at higher electrical dosages, a finding consistent with prior reports and the hypothesis that more effective forms of ECT are more effective in eliciting neurophysiological inhibitory processes.

Authors
West, M; Prado, R; Krystal, AD
MLA Citation
West, M, Prado, R, and Krystal, AD. "Evaluation and Comparison of EEG Traces: Latent Structure in Nonstationary Time Series." Journal of the American Statistical Association 94.448 (1999): 1083-1095.
Source
scival
Published In
Journal of the American Statistical Association
Volume
94
Issue
448
Publish Date
1999
Start Page
1083
End Page
1095

New methods of time series analysis for non-stationary EEG data: Eigenstructure decompositions of time varying autoregressions

Authors
Krystal, AD; Prado, R; West, M
MLA Citation
Krystal, AD, Prado, R, and West, M. "New methods of time series analysis for non-stationary EEG data: Eigenstructure decompositions of time varying autoregressions." Clinical Neurophysiology 110 (1999): 1-10. (Academic Article)
Source
manual
Published In
Clinical Neurophysiology
Volume
110
Publish Date
1999
Start Page
1
End Page
10

Priors and component structures in autoregressive time series models

New approaches to prior specification and structuring in autoregressive time series models are introduced and developed. We focus on defining classes of prior distributions for parameters and latent variables related to latent components of an autoregressive model for an observed time series. These new priors naturally permit the incorporation of both qualitative and quantitative prior information about the number and relative importance of physically meaningful components that represent low frequency trends, quasi-periodic subprocesses and high frequency residual noise components of observed series. The class of priors also naturally incorporates uncertainty about model order and hence leads in posterior analysis to model order assessment and resulting posterior and predictive inferences that incorporate full uncertainties about model order as well as model parameters. Analysis also formally incorporates uncertainty and leads to inferences about unknown initial values of the time series, as it does for predictions of future values. Posterior analysis involves easily implemented iterative simulation methods, developed and described here. One motivating field of application is climatology, where the evaluation of latent structure, especially quasi-periodic structure, is of critical importance in connection with issues of global climatic variability. We explore the analysis of data from the southern oscillation index, one of several series that has been central in recent high profile debates in the atmospheric sciences about recent apparent trends in climatic indicators.

Authors
Huerta, G; West, M
MLA Citation
Huerta, G, and West, M. "Priors and component structures in autoregressive time series models." Journal of the Royal Statistical Society. Series B: Statistical Methodology 61.4 (1999): 881-899.
Source
scival
Published In
Journal of the Royal Statistical Society. Series B: Statistical Methodology
Volume
61
Issue
4
Publish Date
1999
Start Page
881
End Page
899

Evaluation and Comparison of EEG Traces: Latent Structure in Nonstationary Time Series

We explore and illustrate the use of time series decomposition methods for evaluating and comparing latent structure in nonstationary electroencephalographic (EEG) traces obtained from depressed patients during brain seizures induced as part of electroconvulsive therapy (ECT). Analysis of the patterns of change over time in the frequency structure of such EEG data provides insight into the neurophysiological mechanisms of action of this effective but poorly understood antidepressant treatment, and allows clinicians to modify ECT treatments to optimize therapeutic benefits while minimizing associated side effects. Our work has introduced new methods of time-frequency analysis of EEG series that identify the complete pattern of time evolution of frequency structure over the course of a seizure, and usefully assist in these scientific and clinical studies. New methods of decomposition of flexible dynamic models provide time domain decompositions of individual EEG series into collections of latent components in different frequency bands. This allows us to explore ECT seizure characteristics via inferences on the time-varying parameters that characterize these latent components, and to relate differences in such characteristics across seizures to differences in the therapeutic effectiveness and cognitive side effects of those seizures. This article discusses the scientific context and problems, development of nonstationary time series models and new methods of decomposition to explore time-frequency structure, and aspects of model fitting and analysis. We include applied studies on two datasets from recent clinical ECT studies. One is an initial illustrative analysis of a single EEG trace, the second compares the EEG data recorded during two types of ECT treatment that differ in therapeutic effectiveness and cognitive side effects. The uses of these models and time series decomposition methods in extracting and contrasting key features of the seizure underlying the EEG signals are highlighted. Through the use of these models we have quantified, for the first time, decreases in the dominant frequencies of low-frequency EEG components during ECT seizures. We have also identified preliminary evidence that such decreases are enhanced under the more effective ECTs at higher electrical dosages, a finding consistent with prior reports and the hypothesis that more effective forms of ECT are more effective in eliciting neurophysiological inhibitory processes.

Authors
West, M; Prado, R; Krystal, AD
MLA Citation
West, M, Prado, R, and Krystal, AD. "Evaluation and Comparison of EEG Traces: Latent Structure in Nonstationary Time Series." Journal of the American Statistical Association 94.446 (1999): 375-387.
Source
scival
Published In
Journal of the American Statistical Association
Volume
94
Issue
446
Publish Date
1999
Start Page
375
End Page
387

Bayesian inference on latent structure in time series

Authors
Aguilar, O; Huerta, G; Prado, R; West, M
MLA Citation
Aguilar, O, Huerta, G, Prado, R, and West, M. "Bayesian inference on latent structure in time series." 1999.
Source
wos-lite
Published In
BAYESIAN STATISTICS 6
Publish Date
1999
Start Page
3
End Page
26

Rejoinder

Authors
Tebaldi, C; West, M
MLA Citation
Tebaldi, C, and West, M. "Rejoinder." Journal of the American Statistical Association 93.442 (June 1998): 576-576.
Source
crossref
Published In
Journal of the American Statistical Association
Volume
93
Issue
442
Publish Date
1998
Start Page
576
End Page
576
DOI
10.1080/01621459.1998.10473710

Case Studies in Bayesian Statistics IV

Authors
West, M
MLA Citation
West, M. "Case Studies in Bayesian Statistics IV." Springer-Verlag, New York, 1998.
Source
manual
Publish Date
1998

Case Studies in Bayesian Statistics IV

Authors
West, M
MLA Citation
West, M. "Case Studies in Bayesian Statistics IV." Springer-Verlag, New York, 1998.
Source
manual
Publish Date
1998

Computing nonparametric hierarchical models

Authors
Escobar, MD; West, M
MLA Citation
Escobar, MD, and West, M. "Computing nonparametric hierarchical models." Practical Non and Semiparametric Bayesian Statistics. Ed. P Müller, DD Dey, and D Sinha. Springer-Verlag, 1998. 1-16.
Source
manual
Publish Date
1998
Start Page
1
End Page
16

Computing nonparametric hierarchical models

Authors
Escobar, MD; West, M
MLA Citation
Escobar, MD, and West, M. "Computing nonparametric hierarchical models." Practical Non and Semiparametric Bayesian Statistics. Ed. P Müller, DD Dey, and D Sinha. Springer-Verlag, 1998. 1-16.
Source
manual
Publish Date
1998
Start Page
1
End Page
16

Mixture models in the exploration of structure-activity relationships in drug design

Authors
Paddock, S; West, M; Young, S; Clyde, M
MLA Citation
Paddock, S, West, M, Young, S, and Clyde, M. "Mixture models in the exploration of structure-activity relationships in drug design." Springer-Verlag, New York, 1998.
Source
manual
Published In
Case Studies in Bayesian Statistics, Vol. 4
Publish Date
1998
Start Page
339
End Page
354

Mixture models in the exploration of structure-activity relationships in drug design

Authors
Paddock, S; West, M; Young, S; Clyde, M
MLA Citation
Paddock, S, West, M, Young, S, and Clyde, M. "Mixture models in the exploration of structure-activity relationships in drug design." Springer-Verlag, New York, 1998.
Source
manual
Published In
Case Studies in Bayesian Statistics, Vol. 4
Publish Date
1998
Start Page
339
End Page
354

Bayesian forecasting

Authors
West, M
MLA Citation
West, M. "Bayesian forecasting." Encyclopedia of Statistical Sciences. Ed. S Kotz, CB Read, and DL Banks. Wiley, 1998. 50-60.
Source
manual
Publish Date
1998
Start Page
50
End Page
60

Mixture models in the exploration of structure-activity relationships in drug design

We report on a study of mixture modeling problems arising in the assessment of chemical structure-activity relationships in drug design and discovery. Pharmaceutical research laboratories developing test compounds for screening synthesize many related candidate compounds by linking together collections of basic molecular building blocks, known as monomers. These compounds are tested for biological activity, feeding in to screening for further analysis and drug design. The tests also provide data relating compound activity to chemical properties and aspects of the structure of associated monomers, and our focus here is studying such relationships as an aid to future monomer selection. The level of chemical activity of compounds is based on the geometry of chemical binding of test compounds to target binding sites on receptor compounds, but the screening tests are unable to identify binding configurations. Hence potentially critical covariate information is missing as a natural latent variable. Resulting statistical models are then mixed with respect to such missing information, so complicating data analysis and inference. This paper reports on a study of a two-monomer, two-binding site framework and associated data. We build structured mixture models that mix linear regression models, predicting chemical effectiveness, with respect to site-binding selection mechanisms. We discuss aspects of modeling and analysis, including problems and pitfalls, and describe results of analyses of a simulated and real data set. In modeling real data, we are led into critical model extensions that introduce hierarchical random effects components to adequately capture heterogeneities in both the site binding mechanisms and in the resulting levels of effectiveness of compounds once bound. Comments on current and potential future directions conclude the report.

Authors
Paddock, S; Clyde, MA; West, M
MLA Citation
Paddock, S, Clyde, MA, and West, M. "Mixture models in the exploration of structure-activity relationships in drug design." Bayesian Statistics in Science and Engineering: Case Studies IV. Ed. C Gastonis, RE Kass, B Carlin, A Carriquiry, A German, M West, and I Verdinelli. Springer, 1998. 339-353. (Chapter)
Source
manual
Publish Date
1998
Start Page
339
End Page
353
DOI
10.1007/978-1-4612-1502-8_9

Bayesian inference on network traffic using link count data (with discussion)

Authors
Tebaldi, C; West, M
MLA Citation
Tebaldi, C, and West, M. "Bayesian inference on network traffic using link count data (with discussion)." Journal of the American Statistical Association 93 (1998): 557-576. (Academic Article)
Source
manual
Published In
Journal of the American Statistical Association
Volume
93
Publish Date
1998
Start Page
557
End Page
576

Bayesian inference for unequally-spaced time series

Authors
Huerta, G; West, M
MLA Citation
Huerta, G, and West, M. "Bayesian inference for unequally-spaced time series." JSM Proceedings,Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association (1998): 17-21.
Source
manual
Published In
JSM Proceedings,Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association
Publish Date
1998
Start Page
17
End Page
21

Time-frequency decompositions: Bayesian model-based approaches

A range of developments in Bayesian time series modelling in recent years has focussed on issues of identifying latent structure in non-stationary time series, particularly driven by applications in which time-varying spectral structure of time series is an inherent and prime feature. This talk will review some of these developments, including the theoretical and methodological basis of decomposition methods in state-space models. The resulting methods can be viewed as providing a time-domain representation of changing spectral characteristics. Examples will be drawn from problems in clinical EEG studies, where the assessment of changes over time in frequency structure of components of EEG signals is key to characterizing brain seizures under various treatments.

Authors
West, M
MLA Citation
West, M. "Time-frequency decompositions: Bayesian model-based approaches." Conference Record of the Asilomar Conference on Signals, Systems and Computers 1 (1998): 276--.
Source
scival
Published In
Conference Record of the Asilomar Conference on Signals, Systems and Computers
Volume
1
Publish Date
1998
Start Page
276-

Bayesian inference on network traffic using link count data

We study Bayesian models and methods for analysing network traffic counts in problems of inference about the traffic intensity between directed pairs of origins and destinations in networks. This is a class of problems very recently discussed by Vardi in a 1996 JASA article and is of interest in both communication and transportation network studies. The current article develops the theoretical framework of variants of the origin-destination flow problem and introduces Bayesian approaches to analysis and inference. In the first, the so-called fixed routing problem, traffic or messages pass between nodes in a network, with each message originating at a specific source node, and ultimately moving through the network to a predetermined destination node. All nodes are candidate origin and destination points. The framework assumes no travel time complications, considering only the number of messages passing between pairs of nodes in a specified time interval. The route count, or route flow, problem is to infer the set of actual number of messages passed between each directed origin-destination pair in the time interval, based on the observed counts flowing between all directed pairs of adjacent nodes. Based on some development of the theoretical structure of the problem and assumptions about prior distributional forms, we develop posterior distributions for inference on actual origin-destination counts and associated flow rates. This involves iterative simulation methods, or Markov chain Monte Carlo (MCMC), that combine Metropolis-Hastings steps within an overall Gibbs sampling framework. We discuss issues of convergence and related practical matters, and illustrate the approach in a network previously studied in Vardi's article. We explore both methodological and applied aspects much further in a concrete problem of a road network in North Carolina, studied in transportation flow assessment contexts by civil engineers. This investigation generates critical insight into limitations of statistical analysis, and particularly of non-Bayesian approaches, due to inherent structural features of the problem. A truly Bayesian approach, imposing partial stochastic constraints through informed prior distributions, offers a way of resolving these problems and is consistent with prevailing trends in updating traffic flow intensities in this field. Following this, we explore a second version of the problem that introduces elements of uncertainty about routes taken by individual messages in terms of Markov selection of outgoing links for messages at any given node. For specified route choice probabilities, we introduce the concept of a super-network - namely, a fixed routing problem in which the stochastic problem may be embedded. This leads to solution of the stochastic version of the problem using the methods developed for the original formulation of the fixed routing problem. This is also illustrated. Finally, we discuss various related issues and model extensions, including inference on stochastic route choice selection probabilities, questions of missing data and partially observed link counts, and relationships with current research on road traffic network problems in which travel times within links are nonnegligible and may be estimated from additional data.

Authors
Tebaldi, C; West, M
MLA Citation
Tebaldi, C, and West, M. "Bayesian inference on network traffic using link count data." Journal of the American Statistical Association 93.442 (1998): 557-573.
Source
scival
Published In
Journal of the American Statistical Association
Volume
93
Issue
442
Publish Date
1998
Start Page
557
End Page
573

Mixture models in the exploration of structure-activity relationships in drug design

We report on a study of mixture modeling problems arising in the assessment of chemical structure-activity relationships in drug design and discovery. Pharmaceutical research laboratories developing test compounds for screening synthesize many related candidate compounds by linking together collections of basic molecular building blocks, known as monomers. These compounds are tested for biological activity, feeding in to screening for further analysis and drug design. The tests also provide data relating compound activity to chemical properties and aspects of the structure of associated monomers, and our focus here is studying such relationships as an aid to future monomer selection. The level of chemical activity of compounds is based on the geometry of chemical binding of test compounds to target binding sites on receptor compounds, but the screening tests are unable to identify binding configurations. Hence potentially critical covariate information is missing as a natural latent variable. Resulting statistical models are then mixed with respect to such missing information, so complicating data analysis and inference. This paper reports on a study of a two-monomer, two-binding site framework and associated data. We build structured mixture models that mix linear regression models, predicting chemical effectiveness, with respect to site-binding selection mechanisms. We discuss aspects of modeling and analysis, including problems and pitfalls, and describe results of analyses of a simulated and real data set. In modeling real data, we are led into critical model extensions that introduce hierarchical random effects components to adequately capture heterogeneities in both the site binding mechanisms and in the resulting levels of effectiveness of compounds once bound. Comments on current and potential future directions conclude the report.

Authors
Paddock, S; Clyde, MA; West, M
MLA Citation
Paddock, S, Clyde, MA, and West, M. "Mixture models in the exploration of structure-activity relationships in drug design." Bayesian Statistics in Science and Engineering: Case Studies IV. Ed. C Gastonis, RE Kass, B Carlin, A Carriquiry, A German, M West, and I Verdinelli. Springer, 1998. 339-353. (Chapter)
Source
manual
Publish Date
1998
Start Page
339
End Page
353
DOI
10.1007/978-1-4612-1502-8_9

Excitatory synaptic site heterogeneity during paired pulse plasticity in CA1 pyramidal cells in rat hippocampus in vitro.

1. The properties of individual excitatory synaptic sites onto adult CA1 hippocampal neurons were investigated using paired pulse minimal stimulation and low noise whole-cell recordings. Non-NMDA receptor-mediated synaptic responses were isolated using a pharmacological blockade of NMDA and GABAA receptors. Amongst the twenty-five stationary ensembles there were twelve showing paired pulse potentiation, two showing paired pulse depression and eleven with no significant net change. The signal-to-noise ratio averaged 4.5:1. There was no correlation between the amplitude of the first and second responses after separation of failures: the percentage of failures averaged 33.6% for the conditioning pulse and 31.7% for the test pulse. 2. Site-directed Bayesian statistical analysis was developed to predict the likely number of activated synapses, synaptic response amplitudes, probability of release and intrinsic variation at each individual synaptic site. Extensive simulations showed the usefulness of this model and defined appropriate parameters. These simulations demonstrated only small errors in estimating parameters of data sets with a small number of sites (< 10) and similar characteristics to the physiological data sets. 3. Physiological ensembles showed between one and three synaptic sites, which exhibited a wide range of values for release probability (0.03-0.99), synaptic amplitudes (1.46-16.8 pA; approximately 62% coefficient of variation between sites) and intrinsic variation over time (approximately 36%). Paired pulse plasticity occurred primarily from alterations in the release probabilities but a few ensembles also showed small changes in site amplitude. Initial release probability correlated negatively with the degree of paired pulse potentiation. Whilst it was possible to use simple assumptions regarding site homogeneity (such as required for a binomial process) for 48% (12 out of 25) of the data sets, the Bayesian analysis was necessary to reveal the complex changes and heterogeneity that occurred in the other 52% of the data sets. The Bayesian site analysis robustly indicated the presence of considerable site heterogeneity, significant intrinsic site variation over time and changes in parameters at individual synaptic sites with plasticity.

Authors
Turner, DA; Chen, Y; Isaac, JT; West, M; Wheal, HV
MLA Citation
Turner, DA, Chen, Y, Isaac, JT, West, M, and Wheal, HV. "Excitatory synaptic site heterogeneity during paired pulse plasticity in CA1 pyramidal cells in rat hippocampus in vitro." J Physiol 500 ( Pt 2) (April 15, 1997): 441-461.
PMID
9147329
Source
pubmed
Published In
The Journal of Physiology
Volume
500 ( Pt 2)
Publish Date
1997
Start Page
441
End Page
461

Bayesian Forecasting & Dynamic Models

Authors
West, M; Harrison, PJ
MLA Citation
West, M, and Harrison, PJ. Bayesian Forecasting & Dynamic Models. Springer Verlag, 1997.
Source
manual
Publish Date
1997

Bayesian Forecasting & Dynamic Models

Authors
West, M; Harrison, PJ
MLA Citation
West, M, and Harrison, PJ. Bayesian Forecasting & Dynamic Models. Springer Verlag, 1997.
Source
manual
Publish Date
1997

Exploratory modelling of multiple non-stationary time series: Latent process structure and decompositions

Authors
Prado, R; West, M
MLA Citation
Prado, R, and West, M. "Exploratory modelling of multiple non-stationary time series: Latent process structure and decompositions." Modelling Longitudinal and Spatially Correlated Data. Ed. T Gregoire. Springer-Verlag, 1997. 349-362.
Source
manual
Publish Date
1997
Start Page
349
End Page
362

Exploratory modelling of multiple non-stationary time series: Latent process structure and decompositions

Authors
Prado, R; West, M
MLA Citation
Prado, R, and West, M. "Exploratory modelling of multiple non-stationary time series: Latent process structure and decompositions." Modelling Longitudinal and Spatially Correlated Data. Ed. T Gregoire. Springer-Verlag, 1997. 349-362.
Source
manual
Publish Date
1997
Start Page
349
End Page
362

Hierarchical mixture models in neurological transmission analysis

Hierarchically structured mixture models are studied in the context of data analysis and inference on neural synaptic transmission characteristics in mammalian, and other, central nervous systems. Mixture structures arise due to uncertainties about the stochastic mechanisms governing the responses to electrochemical stimulation of individual neurotransmitter release sites at nerve junctions. Models attempt to capture such scientific features as the sensitivity of individual synaptic transmission sites to electrochemical stimuli and the extent of their electrochemical responses when stimulated. This is done via suitably structured classes of prior distributions for parameters describing these features. Such priors may be structured to permit assessment of currently topical scientific hypotheses about fundamental neural function. Posterior analysis is implemented via stochastic simalation. Several data analyses are described to illustrate the approach, with resulting neurophysiological insights in some recently generated experimental contexts. Further developments and open questions, both neurophysiological and statistical, are noted.

Authors
West, M
MLA Citation
West, M. "Hierarchical mixture models in neurological transmission analysis." Journal of the American Statistical Association 92.438 (1997): 587-606.
Source
scival
Published In
Journal of the American Statistical Association
Volume
92
Issue
438
Publish Date
1997
Start Page
587
End Page
606

Heterogeneity between non-NMDA synaptic sites in paired-pulse plasticity of CA1 pyramidal neurons in the hippocampus

Authors
Turner, DA; Chen, Y; Isaac, J; West, M; Wheal, HV
MLA Citation
Turner, DA, Chen, Y, Isaac, J, West, M, and Wheal, HV. "Heterogeneity between non-NMDA synaptic sites in paired-pulse plasticity of CA1 pyramidal neurons in the hippocampus." Journal of Physiology 500 (1997): 441-461. (Academic Article)
Source
manual
Published In
Journal of Physiology
Volume
500
Publish Date
1997
Start Page
441
End Page
461

Time series decomposition

A constructive result on time series decomposition is presented and illustrated. Developed through dynamic linear models, the decomposition is useful in analysis of an observed time series through inference about underlying, latent component series that may have physical interpretations. Particular special cases include state space autoregressive component models, in which the decomposition is useful for isolating latent, quasi-cyclical components, in particular. Brief summaries of analyses of some geological records related to climatic change illustrate the result.

Authors
West, M
MLA Citation
West, M. "Time series decomposition." Biometrika 84.2 (1997): 489-494.
Source
scival
Published In
Biometrika
Volume
84
Issue
2
Publish Date
1997
Start Page
489
End Page
494

Computing distributions of order statistics

Recurrence relationships among the distribution functions of order statistics of independent, but not identically distributed, random quantities are derived. These results extend known theory and provide computationally practicable algorithms for a variety of problems.

Authors
Cao, G; West, M
MLA Citation
Cao, G, and West, M. "Computing distributions of order statistics." Communications in Statistics - Theory and Methods 26.3 (1997): 755-764.
Source
scival
Published In
Communications in Statistics - Theory and Methods
Volume
26
Issue
3
Publish Date
1997
Start Page
755
End Page
764

Bayesian forecasting of multinomial time series through conditionally Gaussian dynamic models

We consider inference in the class of conditionally Gaussian dynamic models for nonnormal multivariate time series. In such models, data are represented as drawn from nonnormal sampling distributions whose parameters are related both through time and hierarchically across several multivariate series. A key example - the main focus here - is time series of multinomial observations, a common occurrence in sociological and demographic studies involving categorical count data. However, we present this development in a more general setting, as the resulting methods apply beyond the multinomial context. We discuss inference in the proposed model class via a posterior simulation scheme based on appropriate modifications of existing Markov chain Monte Carlo algorithms for normal dynamic linear models and including Metropolis-Hastings components. We develop an analysis of time series of flows of students in the Italian secondary education system as an illustration of the models and methods.

Authors
Cargnoni, C; Müller, P; West, M
MLA Citation
Cargnoni, C, Müller, P, and West, M. "Bayesian forecasting of multinomial time series through conditionally Gaussian dynamic models." Journal of the American Statistical Association 92.438 (1997): 640-647.
Source
scival
Published In
Journal of the American Statistical Association
Volume
92
Issue
438
Publish Date
1997
Start Page
640
End Page
647

Bayesian models for non-linear autoregressions

We discuss classes of Bayesian mixture models for nonlinear autoregressive times series, based on developments in semiparametric Bayesian density estimation in recent years. The development involves formal classes of multivariate discrete mixture distributions, providing flexibility in modeling arbitrary nonlinearities in time series structure and a formal inferential framework within which to address the problems of inference and prediction. The models relate naturally to existing kernel and related methods, threshold models and others, although they offer major advances in terms of parameter estimation and predictive calculations. Theoretical and computational aspects are developed here, the latter involving efficient simulation of posterior and predictive distributions. Various examples illustrate our perspectives on identification and inference using this mixture approach.

Authors
Müller, P; West, M; Maceachern, S
MLA Citation
Müller, P, West, M, and Maceachern, S. "Bayesian models for non-linear autoregressions." Journal of Time Series Analysis 18.6 (1997): 593-614.
Source
scival
Published In
Journal of Time Series Analysis
Volume
18
Issue
6
Publish Date
1997
Start Page
593
End Page
614

On Bayesian analysis of mixtures with an unknown number of components - Discussion

Authors
Robert, CP; Aitkin, M; Cox, DR; Stephens, M; Polymenis, A; Gilks, WR; Nobile, A; Hodgson, M; OHagan, A; Longford, NT; Dawid, AP; Atkinson, AC; Bernardo, JM; Besag, J; Brooks, SP; Byers, S; Raftery, A; Celeux, G; Cheng, RCH; Liu, WB; Chien, YH; George, EI; Cressie, N; Huang, HC; Gruet, MA; Heath, SC; Jennison, C; Lawson, AB; Clark, A; McLachlan, G; Peel, D; Mengersen, K; George, A; Philippe, A; Roeder, K; Wasserman, L; Schlattmann, P; Bohning, D; Titterington, DM; Tong, H; West, M
MLA Citation
Robert, CP, Aitkin, M, Cox, DR, Stephens, M, Polymenis, A, Gilks, WR, Nobile, A, Hodgson, M, OHagan, A, Longford, NT, Dawid, AP, Atkinson, AC, Bernardo, JM, Besag, J, Brooks, SP, Byers, S, Raftery, A, Celeux, G, Cheng, RCH, Liu, WB, Chien, YH, George, EI, Cressie, N, Huang, HC, Gruet, MA, Heath, SC, Jennison, C, Lawson, AB, Clark, A, McLachlan, G, Peel, D, Mengersen, K, George, A, Philippe, A, Roeder, K, Wasserman, L, Schlattmann, P, Bohning, D, Titterington, DM, Tong, H, and West, M. "On Bayesian analysis of mixtures with an unknown number of components - Discussion." JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 59.4 (1997): 758-792.
Source
wos-lite
Published In
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Volume
59
Issue
4
Publish Date
1997
Start Page
758
End Page
792

Some statistical issues in Palæoclimatology (with discussion)

Authors
West, M
MLA Citation
West, M. "Some statistical issues in Palæoclimatology (with discussion)." Oxford University Press, 1996.
Source
manual
Published In
Bayesian Statistics 5
Publish Date
1996
Start Page
461
End Page
486

Bayesian curve fitting using multivariate normal mixtures

Problems of regression smoothing and curve fitting are addressed via predictive inference in a flexible class of mixture models. Multidimensional density estimation using Dirichlet mixture models provides the theoretical basis for semi-parametric regression methods in which fitted regression functions may be deduced as means of conditional predictive distributions. These Bayesian regression functions have features similar to generalised kernel regression estimates, but the formal analysis addresses problems of multivariate smoothing, parameter estimation, and the assessment of uncertainties about regression functions naturally. Computations are based on multidimensional versions of existing Markov chain simulation analysis of univariate Dirichlet mixture models.

Authors
Müller, P; Erkanli, A; West, M
MLA Citation
Müller, P, Erkanli, A, and West, M. "Bayesian curve fitting using multivariate normal mixtures." Biometrika 83.1 (1996): 67-79.
Source
scival
Published In
Biometrika
Volume
83
Issue
1
Publish Date
1996
Start Page
67
End Page
79

Practical Bayesian inference using mixtures of mixtures

Discrete mixtures of normal distributions are widely used in modeling amplitude fluctuations of electrical potentials at synapses of human and other animal nervous systems. The usual framework has independent data values y(j) arising as y(j) = μ(j) + x(n(0+j)), where the means μ(j) come from some discrete prior G(μ) and the unknown x(n(0+j))'s and observed x(j), j = 1,...,n0 are Gaussian noise terms. A practically important development of the associated statistical methods is the issue of nonnormality of the noise terms, often the norm rather than the exception in the neurological context. We have recently developed models, based on convolutions of Dirichlet process mixtures, for such problems. Explicitly, we model the noise data values x(j) as arising from a Dirichlet process mixture of normals, in addition to modeling the location prior G(μ) as a Dirichlet process itself. This induces a Dirichlet mixture of mixtures of normals, whose analysis may be developed using Gibbs sampling techniques. We discuss these models and their analysis, and illustrate them in the context of neurological response analysis.

Authors
Cao, G; West, M
MLA Citation
Cao, G, and West, M. "Practical Bayesian inference using mixtures of mixtures." Biometrics 52.4 (1996): 1334-1341.
PMID
8962457
Source
scival
Published In
Biometrics
Volume
52
Issue
4
Publish Date
1996
Start Page
1334
End Page
1341
DOI
10.2307/2532848

Inference in successive sampling discovery models

Successive sampling discovery problems arise in finite population sampling subject to 'size-biased' selection mechanisms. Formal statistical analysis of discovery data under such models is technically challenging. Bayesian analyses are developed here in a superpopulation framework. We show how simulation methods provide computation of posterior distributions for superpopulation parameters and, more critically, predictive inferences for unsampled units in the finite population. Model extensions cover problems of uncertainty about finite population sizes, uncertainty about sample selection mechanisms, and other practical issues. Several analyses of published oil reserve data are used for illustration.

Authors
West, M
MLA Citation
West, M. "Inference in successive sampling discovery models." Journal of Econometrics 75.1 (1996): 217-238.
Source
scival
Published In
Journal of Econometrics
Volume
75
Issue
1
Publish Date
1996
Start Page
217
End Page
238
DOI
10.1016/0304-4076(95)01777-1

Modelling and robustness issues in Bayesian time series analysis

Authors
West, M
MLA Citation
West, M. "Modelling and robustness issues in Bayesian time series analysis." 1996.
Source
wos-lite
Published In
BAYESIAN ROBUSTNESS
Volume
29
Publish Date
1996
Start Page
231
End Page
251
DOI
10.1214/lnms/1215453070

Bayesian time series: Models and computations for the analysis of time series in the physical sciences

Authors
West, M
MLA Citation
West, M. "Bayesian time series: Models and computations for the analysis of time series in the physical sciences." 1996.
Source
wos-lite
Published In
MAXIMUM ENTROPY AND BAYESIAN METHODS
Volume
79
Publish Date
1996
Start Page
23
End Page
34

Bayesian Inference in Cyclical Component Dynamic Linear Models

Authors
West, M
MLA Citation
West, M. "Bayesian Inference in Cyclical Component Dynamic Linear Models." Journal of the American Statistical Association 90.432 (December 1995): 1301-1312.
Source
crossref
Published In
Journal of the American Statistical Association
Volume
90
Issue
432
Publish Date
1995
Start Page
1301
End Page
1312
DOI
10.1080/01621459.1995.10476634

Bayesian inference in cyclical component dynamic linear models

Authors
West, M
MLA Citation
West, M. "Bayesian inference in cyclical component dynamic linear models." JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 90.432 (December 1995): 1301-1312.
Source
wos-lite
Published In
Journal of the American Statistical Association
Volume
90
Issue
432
Publish Date
1995
Start Page
1301
End Page
1312
DOI
10.2307/2291520

Bayesian Density Estimation and Inference Using Mixtures

Authors
Escobar, MD; West, M
MLA Citation
Escobar, MD, and West, M. "Bayesian Density Estimation and Inference Using Mixtures." Journal of the American Statistical Association 90.430 (June 1995): 577-588.
Source
crossref
Published In
Journal of the American Statistical Association
Volume
90
Issue
430
Publish Date
1995
Start Page
577
End Page
588
DOI
10.1080/01621459.1995.10476550

BAYESIAN DENSITY-ESTIMATION AND INFERENCE USING MIXTURES

Authors
ESCOBAR, MD; WEST, M
MLA Citation
ESCOBAR, MD, and WEST, M. "BAYESIAN DENSITY-ESTIMATION AND INFERENCE USING MIXTURES." JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 90.430 (June 1995): 577-588.
Source
wos-lite
Published In
Journal of the American Statistical Association
Volume
90
Issue
430
Publish Date
1995
Start Page
577
End Page
588
DOI
10.2307/2291069

Applied Bayesian Forecasting & Time Series Analysis

Authors
Pole, A; West, M; Harrison, PJ
MLA Citation
Pole, A, West, M, and Harrison, PJ. Applied Bayesian Forecasting & Time Series Analysis. Chapman-Hall, 1994.
Source
manual
Publish Date
1994

Hierarchical priors and mixture models, with application in regression and density estimation

Authors
West, M; Müller, P; Escobar, MD
MLA Citation
West, M, Müller, P, and Escobar, MD. "Hierarchical priors and mixture models, with application in regression and density estimation." Aspects of Uncertainty: A Tribute to D.V. Lindley. Ed. AFM Smith and PR Freeman. London: Wiley, 1994. 363-386.
Source
manual
Publish Date
1994
Start Page
363
End Page
386

Discovery sampling and selection models

Authors
West, M
MLA Citation
West, M. "Discovery sampling and selection models." Springer-Verlag, New York, 1994.
Source
manual
Published In
Statistical Decision Theory and Related Topics
Publish Date
1994
Start Page
221
End Page
235

Discovery sampling and selection models

Authors
West, M
MLA Citation
West, M. "Discovery sampling and selection models." Springer-Verlag, New York, 1994.
Source
manual
Published In
Statistical Decision Theory and Related Topics
Publish Date
1994
Start Page
221
End Page
235

Statistical inference for gravity models in transportation flow forecasting

Authors
West, M
MLA Citation
West, M. Statistical inference for gravity models in transportation flow forecasting. Duke University, 1994.
Source
manual
Publish Date
1994

DECONVOLUTION OF MIXTURES IN ANALYSIS OF NEURAL SYNAPTIC TRANSMISSION

Authors
WEST, M; TURNER, DA
MLA Citation
WEST, M, and TURNER, DA. "DECONVOLUTION OF MIXTURES IN ANALYSIS OF NEURAL SYNAPTIC TRANSMISSION." STATISTICIAN 43.1 (1994): 31-43.
Source
wos-lite
Published In
The Statistician : journal of the Institute of Statisticians
Volume
43
Issue
1
Publish Date
1994
Start Page
31
End Page
43
DOI
10.2307/2348930

Bayesian analysis of mixtures applied to post-synaptic potential fluctuations.

Bayesian inference techniques have been applied to the analysis of fluctuation of post-synaptic potentials in the hippocampus. The underlying statistical model assumes that the varying synaptic signals are characterized by mixtures of (unknown) numbers of individual gaussian, or normal, component distributions. Each solution consists of a group of individual components with unique mean values and relative probabilities of occurrence and a predictive probability density. The advantages of bayesian inference techniques over the alternative method of maximum likelihood estimation (MLE) of the parameters of an unknown mixture distribution include the following: (1) prior information may be incorporated in the estimation of model parameters; (2) conditional probability estimates of the number of individual components in the mixture are calculated; (3) flexibility exists in the extent to which the estimated noise standard deviation indicates the width of each component; (4) posterior distributions for component means are calculated, including measures of uncertainty about the means; and (5) probability density functions of the component distributions and the overall mixture distribution are estimated in relation to the raw grouped data, together with measures of uncertainty about these estimates. This expository report describes this novel approach to the unconstrained identification of components within a mixture, and provides demonstration of the usefulness of the technique in the context of both simulations and the analysis of distributions of synaptic potential signals.

Authors
Turner, DA; West, M
MLA Citation
Turner, DA, and West, M. "Bayesian analysis of mixtures applied to post-synaptic potential fluctuations." J Neurosci Methods 47.1-2 (April 1993): 1-21.
PMID
8321009
Source
pubmed
Published In
Journal of Neuroscience Methods
Volume
47
Issue
1-2
Publish Date
1993
Start Page
1
End Page
21

Assessing mechanisms of neural synaptic activity

Authors
West, M; Cao, G
MLA Citation
West, M, and Cao, G. "Assessing mechanisms of neural synaptic activity." Springer-Verlag, New York, 1993.
Source
manual
Published In
Case Studies in Bayesian Statistics, Vol. 1
Publish Date
1993
Start Page
416
End Page
428

Assessing mechanisms of neural synaptic activity

Authors
West, M; Cao, G
MLA Citation
West, M, and Cao, G. "Assessing mechanisms of neural synaptic activity." Springer-Verlag, New York, 1993.
Source
manual
Published In
Case Studies in Bayesian Statistics, Vol. 1
Publish Date
1993
Start Page
416
End Page
428

Statistical analysis of mixtures applied to postsynaptic potential fluctuations

Authors
Turner, DA; West, M
MLA Citation
Turner, DA, and West, M. "Statistical analysis of mixtures applied to postsynaptic potential fluctuations." Journal of Neuroscience Methods 47 (1993): 1-23. (Academic Article)
Source
manual
Published In
Journal of Neuroscience Methods
Volume
47
Publish Date
1993
Start Page
1
End Page
23

Approximating posterior distributions by mixtures

Authors
West, M
MLA Citation
West, M. "Approximating posterior distributions by mixtures." Journal of the Royal Statistical Society (Ser. B) 54 (1993): 553-568. (Academic Article)
Source
manual
Published In
Journal of the Royal Statistical Society (Ser. B)
Volume
54
Publish Date
1993
Start Page
553
End Page
568

Mixture models, Monte Carlo, Bayesian updating and dynamic models

Authors
West, M
MLA Citation
West, M. "Mixture models, Monte Carlo, Bayesian updating and dynamic models." Computing Science and Statistics 24 (1993): 325-333. (Academic Article)
Source
manual
Published In
Computing Science and Statistics
Volume
24
Publish Date
1993
Start Page
325
End Page
333

APPROXIMATING POSTERIOR DISTRIBUTIONS BY MIXTURES

Authors
WEST, M
MLA Citation
WEST, M. "APPROXIMATING POSTERIOR DISTRIBUTIONS BY MIXTURES." JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL 55.2 (1993): 409-422.
Source
wos-lite
Published In
Journal of the Royal Statistical Society. Series B: Methodological
Volume
55
Issue
2
Publish Date
1993
Start Page
409
End Page
422

A BAYESIAN METHOD FOR CLASSIFICATION AND DISCRIMINATION

Authors
LAVINE, M; WEST, M
MLA Citation
LAVINE, M, and WEST, M. "A BAYESIAN METHOD FOR CLASSIFICATION AND DISCRIMINATION." CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE 20.4 (December 1992): 451-461.
Source
wos-lite
Published In
The Canadian Journal of Statistics
Volume
20
Issue
4
Publish Date
1992
Start Page
451
End Page
461
DOI
10.2307/3315614

MODELING AGENT FORECAST DISTRIBUTIONS

Authors
WEST, M
MLA Citation
WEST, M. "MODELING AGENT FORECAST DISTRIBUTIONS." JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL 54.2 (1992): 553-567.
Source
wos-lite
Published In
Journal of the Royal Statistical Society. Series B: Methodological
Volume
54
Issue
2
Publish Date
1992
Start Page
553
End Page
567

MODELING PROBABILISTIC AGENT OPINION

Authors
WEST, M; CROSSE, J
MLA Citation
WEST, M, and CROSSE, J. "MODELING PROBABILISTIC AGENT OPINION." JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL 54.1 (1992): 285-299.
Source
wos-lite
Published In
Journal of the Royal Statistical Society. Series B: Methodological
Volume
54
Issue
1
Publish Date
1992
Start Page
285
End Page
299

DATA-BASE ERROR TRAPPING AND PREDICTION

Authors
WEST, M; WINKLER, RL
MLA Citation
WEST, M, and WINKLER, RL. "DATA-BASE ERROR TRAPPING AND PREDICTION." JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 86.416 (December 1991): 987-996.
Source
wos-lite
Published In
Journal of the American Statistical Association
Volume
86
Issue
416
Publish Date
1991
Start Page
987
End Page
996
DOI
10.2307/2290515

Data base error trapping and prediction

We develop and analyze models for a class of problems involving inferences about uncertain numbers of errors in data bases. In particular, we study two error detection methods. In the duplicate performance method, all items in a data base are processed by two individuals (or machines), and the resulting records are compared to find disagreements, which are then resolved. In the known errors method, a data base is first extended to include additional items known to be in error, and then the extended data base is checked by a single individual. For both methods, we lay out the underlying structure of the model and generate inferences in terms of predictive distributions for the numbers of undetected errors. The role of prior information is important in these problems of data base quality management. In the first method of error checking, for example, observed data are always equally consistent with small error rates and few remaining errors and with high error rates and many remaining errors. Most of our illustrative analyses use fairly conservative prior specifications, and the results are compared with those in the less formal development of Strayhorn. In practice, of course, appropriately realistic priors should be used, and some possibilities are mentioned. Models of the type studied here are applicable in a wide variety of important practical problems in data quality management, with examples in industrial quality control and reliability control being of particular note. © 1991 Taylor & Francis Group, LLC.

Authors
West, M; Winkler, RL
MLA Citation
West, M, and Winkler, RL. "Data base error trapping and prediction." Journal of the American Statistical Association 86.416 (January 1, 1991): 987-996.
Source
scopus
Published In
Journal of the American Statistical Association
Volume
86
Issue
416
Publish Date
1991
Start Page
987
End Page
996
DOI
10.1080/01621459.1991.10475142

Dynamic linear model diagnostics

SUMMARY: In time series analysis using dynamic linear models, retrospective analysis involves the calculation of filtered, or smoothed, distributions for state parameters in the past. We develop and illustrate novel results that are useful in retrospective assessment of the influence of individual observations on such distributions. In particular, new and computationally simple filtering equations are derived for past state parameters based on leaving out one observation at a time, providing dynamic model based versions of methods currently used in standard, static regression diagnostics. © 1991 Biometrika Trust.

Authors
Harrison, J; West, M
MLA Citation
Harrison, J, and West, M. "Dynamic linear model diagnostics." Biometrika 78.4 (1991): 797-808.
Source
scival
Published In
Biometrika
Volume
78
Issue
4
Publish Date
1991
Start Page
797
End Page
808
DOI
10.1093/biomet/78.4.797

Kernel density estimation and marginalization consistency

Kernel density estimates, as commonly applied, generally have no exact model-based interpretation since they violate conditions that define coherent joint distributions. The issue of marginalization consistency is considered here. It is shown that most commonly used kernel functions violate this condition. It is also shown that marginalization consistency holds only for classes of kernel estimates based on Laplacian, or double-exponential kernels whose window width parameters are appropriately structured. The practical relevance and implications of this result are discussed. © 1991 Biometrika Trust.

Authors
West, M
MLA Citation
West, M. "Kernel density estimation and marginalization consistency." Biometrika 78.2 (1991): 421-425.
Source
scival
Published In
Biometrika
Volume
78
Issue
2
Publish Date
1991
Start Page
421
End Page
425
DOI
10.1093/biomet/78.2.421

EFFICIENT BAYESIAN LEARNING IN NONLINEAR DYNAMIC-MODELS

Authors
POLE, A; WEST, M
MLA Citation
POLE, A, and WEST, M. "EFFICIENT BAYESIAN LEARNING IN NONLINEAR DYNAMIC-MODELS." JOURNAL OF FORECASTING 9.2 (1990): 119-136.
Source
wos-lite
Published In
Journal of Forecasting
Volume
9
Issue
2
Publish Date
1990
Start Page
119
End Page
136
DOI
10.1002/for.3980090205

REFERENCE ANALYSIS OF THE DYNAMIC LINEAR MODEL

Authors
Pole, A; West, M
MLA Citation
Pole, A, and West, M. "REFERENCE ANALYSIS OF THE DYNAMIC LINEAR MODEL." Journal of Time Series Analysis 10.2 (March 1989): 131-147.
Source
crossref
Published In
Journal of Time Series Analysis
Volume
10
Issue
2
Publish Date
1989
Start Page
131
End Page
147
DOI
10.1111/j.1467-9892.1989.tb00020.x

Reference analysis of the DLM

Authors
Pole, A; West, M
MLA Citation
Pole, A, and West, M. "Reference analysis of the DLM." Journal of Time Series Analysis 10 (1989): 131-147. (Academic Article)
Source
manual
Published In
Journal of Time Series Analysis
Volume
10
Publish Date
1989
Start Page
131
End Page
147

Subjective intervention in formal models

Authors
West, M; Harrison, PJ
MLA Citation
West, M, and Harrison, PJ. "Subjective intervention in formal models." Journal of Forecasting 8 (1989): 33-53. (Academic Article)
Source
manual
Published In
Journal of Forecasting
Volume
8
Publish Date
1989
Start Page
33
End Page
53

BAYESIAN STATISTICS, VOL 2, PROCEEDINGS OF THE 2ND VALENCIA INTERNATIONAL MEETING ON BAYESIAN STATISTICS, 6-10 SEPTEMBER, 1983 - BERNARDO,JM, DEGROOT,MH, LINDLEY,DV, SMITH,AFM

Authors
WEST, M
MLA Citation
WEST, M. "BAYESIAN STATISTICS, VOL 2, PROCEEDINGS OF THE 2ND VALENCIA INTERNATIONAL MEETING ON BAYESIAN STATISTICS, 6-10 SEPTEMBER, 1983 - BERNARDO,JM, DEGROOT,MH, LINDLEY,DV, SMITH,AFM." INTERNATIONAL JOURNAL OF FORECASTING 4.4 (1988): 609-611.
Source
wos-lite
Published In
International Journal of Forecasting
Volume
4
Issue
4
Publish Date
1988
Start Page
609
End Page
611
DOI
10.1016/0169-2070(88)90138-0

AN APPLICATION OF DYNAMIC SURVIVAL MODELS IN UNEMPLOYMENT STUDIES

Authors
GAMERMAN, D; WEST, M
MLA Citation
GAMERMAN, D, and WEST, M. "AN APPLICATION OF DYNAMIC SURVIVAL MODELS IN UNEMPLOYMENT STUDIES." JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN 36.2-3 (1987): 269-274.
Source
wos-lite
Published In
The Statistician : journal of the Institute of Statisticians
Volume
36
Issue
2-3
Publish Date
1987
Start Page
269
End Page
274

A time series application of dynamic survival models in unemployment studies

Authors
Gamerman, D; West, M
MLA Citation
Gamerman, D, and West, M. "A time series application of dynamic survival models in unemployment studies." The Statistician 36 (1987): 269-274. (Academic Article)
Source
manual
Published In
The Statistician
Volume
36
Publish Date
1987
Start Page
269
End Page
274

Practical Bayesian forecasting

Authors
Harrison, PJ; West, M
MLA Citation
Harrison, PJ, and West, M. "Practical Bayesian forecasting." The Statistician 36 (1987): 115-125. (Academic Article)
Source
manual
Published In
The Statistician
Volume
36
Publish Date
1987
Start Page
115
End Page
125

BATS: Bayesian Analysis of Time Series

Authors
West, M; Harrison, PJ; Pole, A
MLA Citation
West, M, Harrison, PJ, and Pole, A. "BATS: Bayesian Analysis of Time Series." The Professional Statistician 6 (1987): 43-46. (Academic Article)
Source
manual
Published In
The Professional Statistician
Volume
6
Publish Date
1987
Start Page
43
End Page
46

On scale mixtures of normal distributions

Authors
West, M
MLA Citation
West, M. "On scale mixtures of normal distributions." Biometrika 74 (1987): 646-648. (Academic Article)
Source
manual
Published In
Biometrika
Volume
74
Publish Date
1987
Start Page
646
End Page
648

An analysis of international exchange rates using multivariate DLMs

Authors
Quintana, JM; West, M
MLA Citation
Quintana, JM, and West, M. "An analysis of international exchange rates using multivariate DLMs." The Statistician 36 (1987): 275-281. (Academic Article)
Source
manual
Published In
The Statistician
Volume
36
Publish Date
1987
Start Page
275
End Page
281

INFLUENCE FUNCTIONALS FOR TIME-SERIES - DISCUSSION

Authors
WEST, M
MLA Citation
WEST, M. "INFLUENCE FUNCTIONALS FOR TIME-SERIES - DISCUSSION." ANNALS OF STATISTICS 14.3 (September 1986): 838-840.
Source
wos-lite
Published In
Annals of statistics
Volume
14
Issue
3
Publish Date
1986
Start Page
838
End Page
840
DOI
10.1214/aos/1176350036

Monitoring and Adaptation in Bayesian Forecasting Models

Authors
West, M; Harrison, PJ
MLA Citation
West, M, and Harrison, PJ. "Monitoring and Adaptation in Bayesian Forecasting Models." Journal of the American Statistical Association 81.395 (September 1986): 741-750.
Source
crossref
Published In
Journal of the American Statistical Association
Volume
81
Issue
395
Publish Date
1986
Start Page
741
End Page
750
DOI
10.1080/01621459.1986.10478331

Bayesian model monitoring

Authors
West, M
MLA Citation
West, M. "Bayesian model monitoring." Journal of the Royal Statistical Society (Ser. B) 48 (1986): 70-78. (Academic Article)
Source
manual
Published In
Journal of the Royal Statistical Society (Ser. B)
Volume
48
Publish Date
1986
Start Page
70
End Page
78

Monitoring and adaptation in Bayesian forecasting models

Authors
West, M; Harrison, PJ
MLA Citation
West, M, and Harrison, PJ. "Monitoring and adaptation in Bayesian forecasting models." Journal of the American Statistical Association 81 (1986): 741-750. (Academic Article)
Source
manual
Published In
Journal of the American Statistical Association
Volume
81
Publish Date
1986
Start Page
741
End Page
750

Discussion of: Influence functionals for time series

Authors
West, M
MLA Citation
West, M. "Discussion of: Influence functionals for time series." Annals of Statistics 14 (1986): 838-840. (Academic Article)
Source
manual
Published In
Annals of Statistics
Volume
14
Publish Date
1986
Start Page
838
End Page
840

Dynamic Generalized Linear Models and Bayesian Forecasting

Authors
West, M; Harrison, PJ; Migon, HS
MLA Citation
West, M, Harrison, PJ, and Migon, HS. "Dynamic Generalized Linear Models and Bayesian Forecasting." Journal of the American Statistical Association 80.389 (March 1985): 73-83.
Source
crossref
Published In
Journal of the American Statistical Association
Volume
80
Issue
389
Publish Date
1985
Start Page
73
End Page
83
DOI
10.1080/01621459.1985.10477131

ASPECTS OF BAYESIAN FORECASTING

Authors
HARRISON, PJ; WEST, M
MLA Citation
HARRISON, PJ, and WEST, M. "ASPECTS OF BAYESIAN FORECASTING." JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 36.12 (1985): 1155-1155.
Source
wos-lite
Published In
Journal of the Operational Research Society
Volume
36
Issue
12
Publish Date
1985
Start Page
1155
End Page
1155

Dynamic generalised linear models and Bayesian forecasting (with discussion)

Authors
West, M; Harrison, P; Migon, HS
MLA Citation
West, M, Harrison, P, and Migon, HS. "Dynamic generalised linear models and Bayesian forecasting (with discussion)." Journal of the American Statistical Association 80 (1985): 73-97. (Academic Article)
Source
manual
Published In
Journal of the American Statistical Association
Volume
80
Publish Date
1985
Start Page
73
End Page
97

Outlier models and prior distributions in Bayesian linear regression

Authors
West, M
MLA Citation
West, M. "Outlier models and prior distributions in Bayesian linear regression." Journal of the Royal Statistical Society (Ser. B) 46 (1984): 431-439. (Academic Article)
Source
manual
Published In
Journal of the Royal Statistical Society (Ser. B)
Volume
46
Publish Date
1984
Start Page
431
End Page
439

Bayesian aggregation

Authors
West, M
MLA Citation
West, M. "Bayesian aggregation." Journal of the Royal Statistical Society (Ser. A) 147 (1984): 600-607. (Academic Article)
Source
manual
Published In
Journal of the Royal Statistical Society (Ser. A)
Volume
147
Publish Date
1984
Start Page
600
End Page
607

Detection of renal allograft rejection by computer

Authors
Trimble, I; West, M; Knapp, MS; Pownall, R; Smith, AFM
MLA Citation
Trimble, I, West, M, Knapp, MS, Pownall, R, and Smith, AFM. "Detection of renal allograft rejection by computer." British Medical Journal 286 (1983): 1695-1699. (Academic Article)
Source
manual
Published In
British Medical Journal
Volume
286
Publish Date
1983
Start Page
1695
End Page
1699

Monitoring kidney transplant patients

Authors
Smith, AFM; West, M; Gordon, K; Knapp, MS; Trimble, I
MLA Citation
Smith, AFM, West, M, Gordon, K, Knapp, MS, and Trimble, I. "Monitoring kidney transplant patients." The Statistician 32 (1983): 46-54. (Academic Article)
Source
manual
Published In
The Statistician
Volume
32
Publish Date
1983
Start Page
46
End Page
54

Monitoring renal transplants: An application of the multi-process Kalman filter

Authors
Smith, AFM; West, M
MLA Citation
Smith, AFM, and West, M. "Monitoring renal transplants: An application of the multi-process Kalman filter." Biometrics 39 (1983): 867-878. (Academic Article)
PMID
6367844
Source
manual
Published In
Biometrics
Volume
39
Publish Date
1983
Start Page
867
End Page
878

Robust sequential approximate Bayesian estimation

Authors
West, M
MLA Citation
West, M. "Robust sequential approximate Bayesian estimation." Journal of the Royal Statistical Society (Ser. B) 43 (1981): 157-166. (Academic Article)
Source
manual
Published In
Journal of the Royal Statistical Society (Ser. B)
Volume
43
Publish Date
1981
Start Page
157
End Page
166

Dynamics and sparsity in latent threshold factor models: A study in multivariate EEG signal processing

We discuss Bayesian analysis of multivariate time series with dynamic factor models that exploit time-adaptive sparsity in model parametrizations via the latent threshold approach. One central focus is on the transfer responses of multiple interrelated series to underlying, dynamic latent factor processes. Structured priors on model hyper-parameters are key to the efficacy of dynamic latent thresholding, and MCMC-based computation enables model fitting and analysis. A detailed case study of electroencephalographic (EEG) data from experimental psychiatry highlights the use of latent threshold extensions of time-varying vector autoregressive and factor models. This study explores a class of dynamic transfer response factor models, extending prior Bayesian modeling of multiple EEG series and highlighting the practical utility of the latent thresholding concept in multivariate, non-stationary time series analysis.

Authors
Nakajima, J; West, M
MLA Citation
Nakajima, J, and West, M. "Dynamics and sparsity in latent threshold factor models: A study in multivariate EEG signal processing."
Source
arxiv

Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data

Traffic flow count data in networks arise in many applications, such as automobile or aviation transportation, certain directed social network contexts, and Internet studies. Using an example of Internet browser traffic flow through site-segments of an international news website, we present Bayesian analyses of two linked classes of models which, in tandem, allow fast, scalable and interpretable Bayesian inference. We first develop flexible state-space models for streaming count data, able to adaptively characterize and quantify network dynamics efficiently in real-time. We then use these models as emulators of more structured, time-varying gravity models that allow formal dissection of network dynamics. This yields interpretable inferences on traffic flow characteristics, and on dynamics in interactions among network nodes. Bayesian monitoring theory defines a strategy for sequential model assessment and adaptation in cases when network flow data deviates from model-based predictions. Exploratory and sequential monitoring analyses of evolving traffic on a network of web site-segments in e-commerce demonstrate the utility of this coupled Bayesian emulation approach to analysis of streaming network count data.

Authors
Chen, X; Irie, K; Banks, D; Haslinger, R; Thomas, J; West, M
MLA Citation
Chen, X, Irie, K, Banks, D, Haslinger, R, Thomas, J, and West, M. "Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data."
Source
arxiv

Bayesian emulation for optimization in multi-step portfolio decisions

We discuss the Bayesian emulation approach to computational solution of multi-step portfolio studies in financial time series. "Bayesian emulation for decisions" involves mapping the technical structure of a decision analysis problem to that of Bayesian inference in a purely synthetic "emulating" statistical model. This provides access to standard posterior analytic, simulation and optimization methods that yield indirect solutions of the decision problem. We develop this in time series portfolio analysis using classes of economically and psychologically relevant multi-step ahead portfolio utility functions. Studies with multivariate currency, commodity and stock index time series illustrate the approach and show some of the practical utility and benefits of the Bayesian emulation methodology.

Authors
Irie, K; West, M
MLA Citation
Irie, K, and West, M. "Bayesian emulation for optimization in multi-step portfolio decisions."
Source
arxiv

Bayesian analysis of immune response dynamics with sparse time series data

In vaccine development, the temporal profiles of relative abundance of subtypes of immune cells (T-cells) is key to understanding vaccine efficacy. Complex and expensive experimental studies generate very sparse time series data on this immune response. Fitting multi-parameter dynamic models of the immune response dynamics-- central to evaluating mechanisms underlying vaccine efficacy-- is challenged by data sparsity. The research reported here addresses this challenge. For HIV/SIV vaccine studies in macaques, we: (a) introduce novel dynamic models of progression of cellular populations over time with relevant, time-delayed components reflecting the vaccine response; (b) define an effective Bayesian model fitting strategy that couples Markov chain Monte Carlo (MCMC) with Approximate Bayesian Computation (ABC)-- building on the complementary strengths of the two approaches, neither of which is effective alone; (c) explore questions of information content in the sparse time series for each of the model parameters, linking into experimental design and model simplification for future experiments; and (d) develop, apply and compare the analysis with samples from a recent HIV/SIV experiment, with novel insights and conclusions about the progressive response to the vaccine, and how this varies across subjects.

Authors
Bonassi, FV; Chan, C; West, M
MLA Citation
Bonassi, FV, Chan, C, and West, M. "Bayesian analysis of immune response dynamics with sparse time series data."
Source
arxiv

Dynamic Bayesian Predictive Synthesis in Time Series Forecasting

We discuss model and forecast combination in time series forecasting. A foundational Bayesian perspective based on agent opinion analysis theory defines a new framework for density forecast combination, and encompasses several existing forecast pooling methods. We develop a novel class of dynamic latent factor models for time series forecast synthesis; simulation-based computation enables implementation. These models can dynamically adapt to time-varying biases, miscalibration and inter-dependencies among multiple models or forecasters. A macroeconomic forecasting study highlights the dynamic relationships among synthesized forecast densities, as well as the potential for improved forecast accuracy at multiple horizons.

Authors
McAlinn, K; West, M
MLA Citation
McAlinn, K, and West, M. "Dynamic Bayesian Predictive Synthesis in Time Series Forecasting."
Source
arxiv
Show More