Skip to main content

Mike West

Arts and Sciences Distinguished Professor Emeritus of Statistics and Decision Sciences
Statistical Science
Box 90251, Durham, NC 27708-0251
214C Old Chemistry, Durham, NC 27708

Selected Publications


Bayesian predictive decision synthesis

Journal Article Journal of the Royal Statistical Society. Series B: Statistical Methodology · April 1, 2024 Decision-guided perspectives on model uncertainty expand traditional statistical thinking about managing, comparing, and combining inferences from sets of models. Bayesian predictive decision synthesis (BPDS) advances conceptual and theoretical foundations ... Full text Cite

Reply to Discussions of Multivariate dynamic modeling for Bayesian forecasting of business revenue

Journal Article Applied Stochastic Models in Business and Industry · May 1, 2023 Full text Cite

Hierarchical dynamic modelling for individualized Bayesian forecasting

Journal Article Journal of the Royal Statistical Society. Series C: Applied Statistics · January 1, 2023 We present a case study and methodological developments in large-scale hierarchical dynamic modelling for personalized prediction in commerce. The context is supermarket sales, where improved forecasting of household-specific purchasing behaviour informs d ... Full text Cite

Bayesian Computation in Dynamic Latent Factor Models

Journal Article Journal of Computational and Graphical Statistics · January 1, 2022 Bayesian computation for filtering and forecasting analysis is developed for a broad class of dynamic models. The ability to scale-up such analyses in non-Gaussian, nonlinear multivariate time series models is advanced through the introduction of a novel c ... Full text Cite

Multivariate dynamic modeling for Bayesian forecasting of business revenue

Journal Article Applied Stochastic Models in Business and Industry · January 1, 2022 Forecasting enterprise-wide revenue is critical to many companies and presents several challenges and opportunities for significant business impact. This case study is based on model developments to address these challenges for forecasting in a large-scale ... Full text Cite

Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue

Journal Article Applied Stochastic Models in Business and Industry, forthcoming, 2022 · December 10, 2021 Forecasting enterprise-wide revenue is critical to many companies and presents several challenges and opportunities for significant business impact. This case study is based on model developments to address these challenges for forecasting in a large-scale ... Link to item Cite

Time Series: Modeling, Computation and Inference

Book · July 2021 Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling, Computation, and Inference, Second Edition integrates mainstream approaches for time series modeling with significant recent ... Link to item Cite

Hierarchical Dynamic Modeling for Individualized Bayesian Forecasting

Journal Article · January 9, 2021 We present a case study and methodological developments in large-scale hierarchical dynamic modeling for personalized prediction in commerce. The context is supermarket sales, where improved forecasting of customer/household-specific purchasing behavior in ... Link to item Cite

Bayesian Forecasting of Many Count-Valued Time Series

Journal Article Journal of Business and Economic Statistics · October 1, 2020 We develop and exemplify application of new classes of dynamic models for time series of nonnegative counts. Our novel univariate models combine dynamic generalized linear models for binary and conditionally Poisson time series, with dynamic random effects ... Full text Cite

Perspectives on Constrained Forecasting

Journal Article · July 21, 2020 This expository paper discusses Bayesian decision analysis perspectives on problems of constrained forecasting. Foundational and pedagogic discussion contrasts decision analytic approaches with the traditional, but typically inappropriate, inferential appr ... Link to item Cite

Bayesian Computation in Dynamic Latent Factor Models

Journal Article Journal of Computational and Graphical Statistics, 2021 · July 9, 2020 Bayesian computation for filtering and forecasting analysis is developed for a broad class of dynamic models. The ability to scale-up such analyses in non-Gaussian, nonlinear multivariate time series models is advanced through the introduction of a novel c ... Link to item Cite

Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting

Journal Article Journal of the American Statistical Association · July 2, 2020 We present new methodology and a case study in use of a class of Bayesian predictive synthesis (BPS) models for multivariate time series forecasting. This extends the foundational BPS framework to the multivariate setting, with detailed application in the ... Full text Cite

Probabilistic forecasting of heterogeneous consumer transaction–sales time series

Journal Article International Journal of Forecasting · April 1, 2020 We present new Bayesian methodology for consumer sales forecasting. Focusing on the multi-step-ahead forecasting of daily sales of many supermarket items, we adapt dynamic count mixture models for forecasting individual customer transactions, and introduce ... Full text Cite

Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions

Journal Article Annals of the Institute of Statistical Mathematics · February 1, 2020 I discuss recent research advances in Bayesian state-space modeling of multivariate time series. A main focus is on the “decouple/recouple” concept that enables application of state-space models to increasingly large-scale data, applying to continuous or d ... Full text Cite

Bayesian dynamic modeling and monitoring of network flows

Journal Article Network Science · September 1, 2019 In the context of a motivating study of dynamic network flow data on a large-scale e-commerce website, we develop Bayesian models for online/sequential analysis for monitoring and adapting to changes reflected in node-node traffic. For large-scale networks ... Full text Cite

Adaptive Variable Selection for Sequential Prediction in Multivariate Dynamic Models

Journal Article Bayesian Analysis, 16:1059-1083, 2021 · June 15, 2019 We discuss Bayesian model uncertainty analysis and forecasting in sequential dynamic modeling of multivariate time series. The perspective is that of a decision-maker with a specific forecasting objective that guides thinking about relevant models. Based o ... Link to item Cite

VARIABLE PRIORITIZATION IN NONLINEAR BLACK BOX METHODS: A GENETIC ASSOCIATION CASE STUDY1.

Journal Article The annals of applied statistics · June 2019 The central aim in this paper is to address variable selection questions in nonlinear and nonparametric regression. Motivated by statistical genetics, where nonlinear interactions are of particular interest, we introduce a novel and interpretable way to su ... Full text Cite

Dynamic Bayesian predictive synthesis in time series forecasting

Journal Article Journal of Econometrics · May 1, 2019 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 met ... Full text Cite

Bayesian emulation for multi-step optimization in decision problems

Journal Article Bayesian Analysis · January 1, 2019 We develop a Bayesian approach to computational solution of multistep optimization problems, highlighted in the example of financial portfolio decisions. The approach involves mapping the technical structure of a decision analysis problem to that of Bayesi ... Full text Cite

Scalable Bayesian Modeling, Monitoring, and Analysis of Dynamic Network Flow Data

Journal Article Journal of the American Statistical Association · April 3, 2018 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 int ... Full text Cite

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

Journal Article Brazilian Journal of Probability and Statistics · January 1, 2017 We discuss Bayesian analysis of multivariate time series with dynamic factor models that exploit time-adaptive sparsity in model parametriza-tions via the latent threshold approach. One central focus is on the transfer responses of multiple interrelated se ... Full text Cite

Bayesian analysis of immune response dynamics with sparse time series data

Journal Article · June 28, 2016 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. Fi ... Link to item Cite

Dynamic dependence networks: Financial time series forecasting and portfolio decisions

Journal Article Applied Stochastic Models in Business and Industry · May 1, 2016 We discuss Bayesian forecasting of increasingly high-dimensional time series, a key area of application of stochastic dynamic models in the financial industry and allied areas of business. Novel state-space models characterizing sparse patterns of dependen ... Full text Cite

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

Journal Article Applied Stochastic Models in Business and Industry · May 1, 2016 Researchers provide a rejoinder to discussion on 'Dynamic dependence networks: Financial time series forecasting and portfolio decisions'. The authors state that there is a need to consider more formal approaches to defining ordering(s) for evaluation. One ... Full text Cite

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

Journal Article Biostatistics · January 2016 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 desig ... Full text Link to item Cite

Models of random sparse eigenmatrices and Bayesian analysis of multivariate structure

Conference Abel Symposia · January 1, 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 ... Full text Cite

Dynamic network signal processing using latent threshold models

Journal Article Digital Signal Processing: A Review Journal · December 1, 2015 We discuss multivariate time series signal processing that exploits a recently introduced approach to dynamic sparsity modelling based on latent thresholding. This methodology induces time-varying patterns of zeros in state parameters that define both dire ... Full text Cite

Sequential monte carlo with adaptive weights for approximate bayesian computation

Report · January 1, 2015 Methods of approximate Bayesian computation (ABC) are increasingly used for analysis of complex models. A major challenge for ABC is overcoming the often inherent problem of high rejection rates in the accept/reject methods based on prior:predictive sampli ... Full text Cite

Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models

Journal Article International Journal of Forecasting · January 1, 2014 We extend the recently introduced latent threshold dynamic models to include dependencies among the dynamic latent factors which underlie multivariate volatility. With an ability to induce time-varying sparsity in factor loadings, these models now also all ... Full text Cite

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

Journal Article Annals of the Institute of Statistical Mathematics · 2014 Full text Cite

Bayesian Forecasting and Dynamic Models

Book · June 29, 2013 ... Bayesian forecasting and dynamic models / Mike West and Jeff Harrison. p. cm .—(Springer series in statistics) Bibliography: p. (U.S. : alk. paper) 1. Bayesian statistical decision theory. 2. Linear models (Statistics) I. Harrison, Jeff. II. Title. II ... Cite

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

Journal Article Stat Appl Genet Mol Biol · June 2013 Novel uses of automated flow cytometry technology for measuring levels of protein markers on thousands to millions of cells are promoting increasing need for relevant, customized Bayesian mixture modelling approaches in many areas of biomedical research an ... Full text Link to item Cite

Bayesian analysis of latent threshold dynamic models

Journal Article Journal of Business & Economic Statistics · 2013 Featured Publication Full text Cite

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

Journal Article PLoS Comput Biol · 2013 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). Stan ... Full text Link to item Cite

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

Journal Article Journal of the American Statistical Association · December 12, 2012 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 i ... Full text Cite

Rejoinder

Journal Article Journal of the American Statistical Association · December 12, 2012 Full text Cite

Dynamic factor volatility modeling: A bayesian latent threshold approach

Journal Article Journal of Financial Econometrics · December 1, 2012 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 rela ... Full text Cite

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

Journal Article Journal of electronic imaging · June 2012 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 si ... Full text Cite

Preface

Chapter · January 19, 2012 Full text Cite

Bayesian Statistics 9

Conference Bayesian Statistics 9 · January 19, 2012 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 Ni ... Full text Cite

Bayesian learning from marginal data in bionetwork models.

Journal Article Statistical applications in genetics and molecular biology · October 2011 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 intracel ... Full text Cite

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

Journal Article PLoS computational biology · October 2011 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, ... Full text Cite

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

Journal Article Atmospheric Chemistry and Physics · July 15, 2011 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 sour ... Full text Cite

Origin of bistability underlying mammalian cell cycle entry.

Journal Article Molecular systems biology · April 2011 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 sign ... Full text Cite

Efficient Classification-Based Relabeling in Mixture Models.

Journal Article The American statistician · February 2011 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 ... Full text Cite

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

Journal Article Cytometry A · December 2010 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 ... Full text Link to item Cite

Rejoinder

Journal Article Bayesian Analysis · December 1, 2010 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 conn ... Full text Cite

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

Journal Article PLoS Genet · September 2, 2010 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 c ... Full text Open Access Link to item Cite

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

Journal Article Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · June 2010 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 me ... Full text Open Access Cite

Understanding GPU Programming for Statistical Computation: Studies in Massively Parallel Massive Mixtures.

Journal Article J Comput Graph Stat · June 1, 2010 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 fitt ... Full text Open Access Link to item Cite

Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing.

Journal Article Journal of machine learning research : JMLR · May 2010 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 s ... Open Access Cite

Time series: Modeling, computation, and inference

Book · January 1, 2010 Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology a ... Full text Cite

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

Journal Article Bulletin of the International Society for Bayesian Analysis · 2010 Cite

Bayesian forecasting

Chapter · 2010 Cite

Bayesian learning in sparse graphical factor models via annealed entropy

Journal Article Journal of Machine Learning Research · 2010 Cite

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

Journal Article Bayesian Analysis · 2010 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 be ... Full text Cite

Discriminative information analysis in mixture modelling

Journal Article Working Paper, Department of Statistical Science, Duke University · 2010 Link to item Cite

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

Journal Article Cytometry. Part A : the journal of the International Society for Analytical Cytology · January 2010 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 singl ... Full text Cite

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

Journal Article Bayesian analysis · January 2010 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 be ... Cite

The Oxford Handbook of Applied Bayesian Analysis

Book · 2010 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 Baye ... Cite

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

Journal Article Bayesian analysis · December 2009 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. ... Full text Cite

A dynamic modelling strategy for bayesian computer model emulation

Journal Article Bayesian Analysis · December 1, 2009 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 ... Full text Cite

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

Journal Article Molecular cell · April 2009 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 ref ... Full text Cite

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

Journal Article PLoS ONE · February 19, 2009 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 ... Full text Cite

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

Journal Article Statistical applications in genetics and molecular biology · January 2009 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 downstrea ... Full text Cite

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

Journal Article PLoS One · 2009 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 " ... Full text Link to item Cite

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

Journal Article Ann Appl Stat · 2009 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 tra ... Full text Link to item Cite

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

Journal Article JSM Proceedings,Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association · 2009 Cite

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

Journal Article PLoS Genet · December 2008 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 ... Full text Link to item Cite

Statistical mixture modeling for cell subtype identification in flow cytometry.

Journal Article Cytometry A · August 2008 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 mix ... Full text Link to item Cite

Of cardiovascular illness and diversity of biological response.

Journal Article Trends in cardiovascular medicine · July 2008 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 ... Full text Cite

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

Journal Article Statistical Methodology · May 1, 2008 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 patie ... Full text Cite

High-dimensional sparse factor modelling - Applications in gene expression genomics

Journal Article Journal of the American Statistical Association · 2008 Cite

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

Journal Article BMC genomics · January 2008 BackgroundThe 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 b ... Full text Cite

Dynamic matrix-variate graphical models

Journal Article Bayesian Analysis · December 1, 2007 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 indep ... Full text Cite

Understanding the use of unlabelled data in predictive modelling

Journal Article Statistical Science · October 2007 Cite

Simulation of hyper-inverse Wishart distributions in graphical models

Journal Article Biometrika · September 14, 2007 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 W ... Full text Cite

Of mice and men: Sparse statistical modeling in cardiovascular genomics

Journal Article The Annals of Applied Statistics · June 1, 2007 Full text Cite

Shotgun stochastic search for "large p" regression

Journal Article Journal of the American Statistical Association · June 1, 2007 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 ... Full text Cite

The use of unlabeled data in predictive modeling

Journal Article Statistical Science · May 1, 2007 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 t ... Full text Cite

Bayesian CART: Prior specification and posterior simulation

Journal Article Journal of Computational and Graphical Statistics · March 1, 2007 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 combinatio ... Full text Cite

Dynamic matrix-variate graphical models

Journal Article Bayesian Analysis · 2007 Cite

Non-parametric Bayesian kernel models

Journal Article Department of Statistical Science, Duke University · 2007 Cite

Bayesian CART: Prior structure and MCMC computations

Journal Article Journal of Computational and Graphical Statistics · 2007 Cite

Shotgun stochastic search in regression with many predictors

Journal Article Journal of the American Statistical Association · 2007 Cite

SSS: High-dimensional Bayesian regression model search

Journal Article Bulletin of the International Society for Bayesian Analysis · 2007 Cite

BFRM: Bayesian factor regression modelling

Journal Article Bulletin of the International Society for Bayesian Analysis · 2007 Cite

Multi-scale and hidden resolution time series models

Journal Article Bayesian Analysis · December 1, 2006 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 ... Full text Cite

Genomic prediction of locoregional recurrence after mastectomy in breast cancer.

Journal Article J Clin Oncol · October 1, 2006 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 ... Full text Link to item Cite

Embracing the complexity of genomic data for personalized medicine.

Journal Article Genome Res · May 2006 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 pers ... Full text Link to item Cite

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

Journal Article Int J Radiat Oncol Biol Phys · April 1, 2006 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 1,01 ... Full text Link to item Cite

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

Journal Article Journal of Statistical Planning and Inference · February 1, 2006 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 parameter ... Full text Cite

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

Journal Article Clin Cancer Res · February 1, 2006 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 ... Full text Link to item Cite

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

Journal Article Nature · January 19, 2006 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 ... Full text Link to item Cite

Multiscale and hidden resolution time series models

Journal Article Bayesian Analysis · 2006 Cite

High-dimensional regression in cancer genomics

Journal Article Bulletin of the International Society for Bayesian Analysis · 2006 Cite

Covariance decomposition in undirected Gaussian graphical models

Journal Article Biometrika · December 1, 2005 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 ar ... Full text Cite

Molecular evidence for arterial repair in atherosclerosis.

Journal Article Proc Natl Acad Sci U S A · November 15, 2005 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 d ... Full text Link to item Cite

Experiments in stochastic computation for high-dimensional graphical models

Journal Article Statistical Science · November 1, 2005 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 di ... Full text Cite

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

Journal Article Proc Natl Acad Sci U S A · November 1, 2005 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 pr ... Full text Link to item Cite

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

Conference CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION · November 1, 2005 Featured Publication Link to item Cite

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

Journal Article Bioinformatics · July 1, 2005 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 ... Full text Link to item Cite

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

Journal Article J Clin Oncol · June 2005 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 sig ... Link to item Cite

Gene expression profiling and genetic markers in glioblastoma survival.

Journal Article Cancer Res · May 15, 2005 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 biologic ... Full text Link to item Cite

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

Journal Article Clin Cancer Res · May 15, 2005 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 wi ... Full text Link to item Cite

Physiological and statistical approaches to modeling of synaptic responses

Chapter · January 1, 2005 Transmission of signals across synapses is the central component of nervous system function (Bennett and Kearns, 2000; Manwani and Koch, 2000). Primarily, such synaptic transmission is mediated by chemical neurotransmitter substances that are released into ... Cite

Gene expression phenotypes of atherosclerosis.

Journal Article Arterioscler Thromb Vasc Biol · October 2004 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 susceptibili ... Full text Link to item Cite

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

Journal Article Biostatistics · October 2004 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 bin ... Full text Link to item Cite

An overview of genomic data analysis.

Journal Article Surgery · September 2004 Full text Link to item Cite

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

Journal Article Proc Natl Acad Sci U S A · June 1, 2004 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 ... Full text Link to item Cite

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

Journal Article Am J Obstet Gynecol · April 2004 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 ... Full text Link to item Cite

Monte carlo smoothing for nonlinear time series

Journal Article Journal of the American Statistical Association · March 1, 2004 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 ... Full text Cite

Bayesian model assessment in factor analysis

Journal Article Statistica Sinica · January 1, 2004 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 o ... Cite

Sparse graphical models for exploring gene expression data

Journal Article Journal of Multivariate Analysis · January 1, 2004 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 ... Full text Cite

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

Journal Article Hum Mol Genet · October 15, 2003 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 be ... Full text Link to item Cite

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

Journal Article Cancer Res · July 1, 2003 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 par ... Link to item Cite

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

Journal Article Nat Genet · June 2003 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 ... Full text Link to item Cite

Gene expression predictors of breast cancer outcomes.

Journal Article Lancet · May 10, 2003 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 ... Full text Link to item Cite

Randomized Polya tree models for nonparametric Bayesian inference

Journal Article Statistica Sinica · April 1, 2003 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 ... Cite

Multiscale modelling of 1-D permeability fields

Conference Bayesian Statistics 7 · 2003 Cite

Tree models for nonparametric Bayesian inference

Journal Article Statistica Sinica · 2003 Cite

Prediction tree models in clinico-genomics

Journal Article Bulletin of the International Statistical Institute · 2003 Cite

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

Journal Article Recent Prog Horm Res · 2003 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 met ... Full text Link to item Cite

Monte Carlo smoothing with application to audio signal enhancement

Journal Article IEEE Transactions on Signal Processing · February 1, 2002 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 le ... Full text Cite

Prediction and uncertainty in the analysis of gene expression profiles.

Journal Article In Silico Biol · 2002 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 ... Link to item Cite

Statistical analyses of freeway traffic flows

Journal Article Journal of Forecasting · January 1, 2002 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 part ... Full text Cite

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

Journal Article Technometrics · January 1, 2002 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 c ... Full text Cite

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

Journal Article Proc Natl Acad Sci U S A · September 25, 2001 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 ... Full text Link to item Cite

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

Journal Article Mol Cell Biol · July 2001 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 fibro ... Full text Link to item Cite

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

Journal Article Journal of the Royal Statistical Society (Ser. C) · 2001 Cite

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

Journal Article Journal of the Royal Statistical Society. Series C: Applied Statistics · January 1, 2001 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 trac ... Full text Cite

Priors and Component Structures in Autoregressive Time Series Models

Journal Article Journal of the Royal Statistical Society Series B: Statistical Methodology · July 1, 2000 Full text Cite

EEG effects of ECT: implications for rTMS.

Journal Article Depress Anxiety · 2000 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 ne ... Full text Link to item Cite

Profiling mental health provider trends in health care delivery systems

Journal Article Health Services & Outcomes Research Methodology · 2000 Cite

Bayesian dynamic factor models and portfolio allocation

Journal Article Journal of Business and Economic Statistics · January 1, 2000 Featured Publication 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 th ... Full text Cite

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

Journal Article ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings · January 1, 2000 We develop methods for performing filtering and smoothing in nonlinear 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 idea ... Full text Cite

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

Journal Article Clin Neurophysiol · December 1999 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 ... Full text Link to item Cite

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

Journal Article Journal of the American Statistical Association · December 1, 1999 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 electroconv ... Full text Cite

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

Journal Article Journal of the American Statistical Association · June 1, 1999 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 electroconv ... Full text Cite

Bayesian Forecasting and Dynamic Models

Book · March 26, 1999 This text is concerned with Bayesian learning, inference and forecasting in dynamic environments. ... Cite

Analysis of hospital quality monitors using hierarchical time series models

Conference Case Studies in Bayesian Statistics, Vol. 4 · 1999 Cite

Bayesian inference on periodicities and component spectral structure in time series

Journal Article Journal of Time Series Analysis · January 1, 1999 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 ... Full text Cite

Priors and component structures in autoregressive time series models

Journal Article Journal of the Royal Statistical Society. Series B: Statistical Methodology · January 1, 1999 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 autoregressi ... Full text Cite

Bayesian inference on latent structure in time series

Conference BAYESIAN STATISTICS 6 · January 1, 1999 Link to item Cite

Time-frequency decompositions: Bayesian model-based approaches

Journal Article Conference Record of the Asilomar Conference on Signals, Systems and Computers · December 1, 1998 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 a ... Cite

Rejoinder

Journal Article Journal of the American Statistical Association · June 1, 1998 Full text Cite

Bayesian inference on network traffic using link count data

Journal Article Journal of the American Statistical Association · June 1, 1998 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 ... Full text Cite

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

Conference Case Studies in Bayesian Statistics, Vol. 4 · 1998 Cite

Bayesian forecasting

Chapter · 1998 Cite

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

Chapter · 1998 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 cand ... Full text Cite

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

Chapter · 1998 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 cand ... Full text Cite

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

Journal Article Journal of the American Statistical Association · 1998 Cite

Bayesian inference for unequally-spaced time series

Journal Article JSM Proceedings,Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association · 1998 Cite

Hierarchical Mixture Models in Neurological Transmission Analysis

Journal Article Journal of the American Statistical Association · June 1, 1997 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 sto ... Full text Cite

Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models

Journal Article Journal of the American Statistical Association · June 1, 1997 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 hiera ... Full text Cite

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

Journal Article J Physiol · April 15, 1997 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 p ... Full text Link to item Cite

Time series decomposition

Journal Article Biometrika · January 1, 1997 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 ma ... Full text Cite

Computing distributions of order statistics

Journal Article Communications in Statistics - Theory and Methods · January 1, 1997 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 ... Full text Cite

Bayesian models for non-linear autoregressions

Journal Article Journal of Time Series Analysis · January 1, 1997 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 distribut ... Full text Cite

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

Journal Article JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY · January 1, 1997 Link to item Cite

Practical Bayesian inference using mixtures of mixtures.

Journal Article Biometrics · December 1996 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 yj arising as yj = mu j + xn0 + j, ... Full text Cite

Bayesian curve fitting using multivariate normal mixtures

Journal Article Biometrika · January 1, 1996 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 regressio ... Full text Cite

Inference in successive sampling discovery models

Journal Article Journal of Econometrics · January 1, 1996 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 ... Full text Cite

Bayesian Inference in Cyclical Component Dynamic Linear Models

Journal Article Journal of the American Statistical Association · December 1995 Full text Cite

Bayesian Density Estimation and Inference Using Mixtures

Journal Article Journal of the American Statistical Association · June 1995 Full text Cite

Bayesian density estimation and inference using mixtures

Journal Article Journal of the American Statistical Association · January 1, 1995 We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dirichlet processes. These models provide natural settings for density estimation and are exemplified by special cases where data are modeled as a sample from ... Full text Cite

Bayesian inference in cyclical component dynamic linear models

Journal Article Journal of the American Statistical Association · January 1, 1995 Dynamic linear models (DLM’s) with time-varying cyclical components are developed for the analysis of time series with persistent though time-varying cyclical behavior. The development covers inference on wavelengths of possibly several persistent cycles i ... Full text Cite

Discovery sampling and selection models

Conference Statistical Decision Theory and Related Topics · 1994 Cite

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

Journal Article J Neurosci Methods · April 1993 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 ... Full text Link to item Cite

Assessing mechanisms of neural synaptic activity

Conference Case Studies in Bayesian Statistics, Vol. 1 · 1993 Cite

Statistical analysis of mixtures applied to postsynaptic potential fluctuations

Journal Article Journal of Neuroscience Methods · 1993 Cite

Approximating posterior distributions by mixtures

Journal Article Journal of the Royal Statistical Society (Ser. B) · 1993 Cite

Mixture models, Monte Carlo, Bayesian updating and dynamic models

Journal Article Computing Science and Statistics · 1993 Cite

APPROXIMATING POSTERIOR DISTRIBUTIONS BY MIXTURES

Journal Article JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL · January 1, 1993 Link to item Cite

MODELING AGENT FORECAST DISTRIBUTIONS

Journal Article JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL · January 1, 1992 Link to item Cite

MODELING PROBABILISTIC AGENT OPINION

Journal Article JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL · January 1, 1992 Link to item Cite

A Bayesian method for classification and discrimination

Journal Article Canadian Journal of Statistics · January 1, 1992 We discuss Bayesian analyses of traditional normal‐mixture models for classification and discrimination. The development involves application of an iterative resampling approach to Monte Carlo inference, commonly called Gibbs sampling, and demonstrates rou ... Full text Cite

Dynamic linear model diagnostics

Journal Article Biometrika · December 1, 1991 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 ... Full text Cite

Kernel density estimation and marginalization consistency

Journal Article Biometrika · June 1, 1991 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 com ... Full text Cite

DATA-BASE ERROR TRAPPING AND PREDICTION

Journal Article JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION · 1991 Full text Cite

Efficient bayesian learning in non‐linear dynamic models

Journal Article Journal of Forecasting · January 1, 1990 This paper demonstrates the practical application of recently developed techniques of efficient numerical analysis for dynamic models. The models presented share a common basic structural foundation but nevertheless cover a very large arena of possible app ... Full text Cite

REFERENCE ANALYSIS OF THE DYNAMIC LINEAR MODEL

Journal Article Journal of Time Series Analysis · January 1, 1989 Abstract. We consider the analysis of normal dynamic linear models subject to reference (vague or uninformative) initial priors on the state parameters and observational variance. New sequential updating equations and the related smoothing or filtering rec ... Full text Cite

Reference analysis of the DLM

Journal Article Journal of Time Series Analysis · 1989 Cite

Subjective intervention in formal models

Journal Article Journal of Forecasting · 1989 Cite

AN APPLICATION OF DYNAMIC SURVIVAL MODELS IN UNEMPLOYMENT STUDIES

Journal Article JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN · January 1, 1987 Link to item Cite

Practical Bayesian forecasting

Journal Article The Statistician · 1987 Cite

BATS: Bayesian Analysis of Time Series

Journal Article The Professional Statistician · 1987 Cite

On scale mixtures of normal distributions

Journal Article Biometrika · 1987 Cite

Discussion: Influence Functionals for Time Series

Journal Article The Annals of Statistics · September 1, 1986 Full text Cite

Monitoring and adaptation in Bayesian forecasting models

Journal Article Journal of the American Statistical Association · January 1, 1986 Practical aspects of a new technique for monitoring and controlling the predictive performance of Bayesian forecasting models are discussed. The basic features of the approach to model monitoring introduced in a general setting in West (1986) are described ... Full text Cite

Bayesian model monitoring

Journal Article Journal of the Royal Statistical Society (Ser. B) · 1986 Cite

Monitoring and adaptation in Bayesian forecasting models

Journal Article Journal of the American Statistical Association · 1986 Cite

Discussion of: Influence functionals for time series

Journal Article Annals of Statistics · 1986 Cite

ASPECTS OF BAYESIAN FORECASTING

Journal Article JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY · January 1, 1985 Link to item Cite

Dynamic generalized linear models and Bayesian forecasting

Journal Article Journal of the American Statistical Association · January 1, 1985 Dynamic Bayesian models are developed for application in nonlinear, non-normal time series and regression problems, providing dynamic extensions of standard generalized linear models. A key feature of the analysis is the use of conjugate prior and posterio ... Full text Cite

Rejoinder

Journal Article Journal of the American Statistical Association · January 1, 1985 Full text Cite

Dynamic generalised linear models and Bayesian forecasting (with discussion)

Journal Article Journal of the American Statistical Association · 1985 Cite

Outlier models and prior distributions in Bayesian linear regression

Journal Article Journal of the Royal Statistical Society (Ser. B) · 1984 Cite

Bayesian aggregation

Journal Article Journal of the Royal Statistical Society (Ser. A) · 1984 Link to item Cite

Detection of renal allograft rejection by computer

Journal Article British Medical Journal · 1983 Cite

Monitoring kidney transplant patients

Journal Article The Statistician · 1983 Cite

The detection of sudden change in renal function by time-series analysis, including the use of the Kalman Filter - a new statistical approach with potential applications for chronobiologists

Journal Article Advances in the Biosciences · December 1, 1982 During the development of renal failure body fluid constituents remain in a dynamic state, with fluctuations in the composition of body fluids resulting from food intake, circadian variations in renal function and other factors. There are progressive rises ... Cite

Robust sequential approximate Bayesian estimation

Journal Article Journal of the Royal Statistical Society (Ser. B) · 1981 Cite