Case studies in bayesian statistical modelling and analysis

Case Studies in Bayesian Statistical Modelling and Analysis WILEY SERIES IN PROBABILITY AND STATISTICS Established by WALTER A SHEWHART and SAMUEL S WILKS Editors David J Balding, Noel A.C Cressie, Garrett M Fitzmaurice, Harvey Goldstein, Iain M Johnstone, Geert Molenberghs, David W Scott, Adrian F.M Smith, Ruey S Tsay, Sanford Weisberg Editors Emeriti Vic Barnett, Ralph A Bradley, J Stuart Hunter, J.B Kadane, David G Kendall, Jozef L Teugels A complete list of the titles in this series appears at the end of this volume Case Studies in Bayesian Statistical Modelling and Analysis Edited by Clair L Alston, Kerrie L Mengersen and Anthony N Pettitt Queensland University of Technology, Brisbane, Australia This edition first published 2013 © 2013 John Wiley & Sons, Ltd Registered office John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com The right of the author to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988 All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher Wiley also publishes its books in a variety of electronic formats Some content that appears in print may not be available in electronic books Designations used by companies to distinguish their products are often claimed as trademarks All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners The publisher is not associated with any product or vendor mentioned in this book This publication is designed to provide accurate and authoritative information in regard to the subject matter covered It is sold on the understanding that the publisher is not engaged in rendering professional services If professional advice or other expert assistance is required, the services of a competent professional should be sought Library of Congress Cataloging-in-Publication Data Case studies in Bayesian statistical modelling and analysis / edited by Clair Alston, Kerrie Mengersen, and Anthony Pettitt pages cm Includes bibliographical references and index ISBN 978-1-119-94182-8 (cloth) Bayesian statistical decision theory I Alston, Clair II Mengersen, Kerrie L III Pettitt, Anthony (Anthony N.) QA279.5.C367 2013 519.5’42–dc23 2012024683 A catalogue record for this book is available from the British Library ISBN: 978-1-119-94182-8 Typeset in 10/12pt Times Roman by Thomson Digital, Noida, India Contents Preface xvii List of contributors xix Introduction Clair L Alston, Margaret Donald, Kerrie L Mengersen and Anthony N Pettitt 1.1 1.2 1.3 Introduction Overview Further reading 1.3.1 Bayesian theory and methodology 1.3.2 Bayesian methodology 1.3.3 Bayesian computation 1.3.4 Bayesian software 1.3.5 Applications References 1 8 10 10 11 13 13 Introduction to MCMC Anthony N Pettitt and Candice M Hincksman 17 2.1 2.2 Introduction Gibbs sampling 2.2.1 Example: Bivariate normal 2.2.2 Example: Change-point model 2.3 Metropolis–Hastings algorithms 2.3.1 Example: Component-wise MH or MH within Gibbs 2.3.2 Extensions to basic MCMC 2.3.3 Adaptive MCMC 2.3.4 Doubly intractable problems 2.4 Approximate Bayesian computation 2.5 Reversible jump MCMC 2.6 MCMC for some further applications References 17 18 18 19 19 20 21 22 22 24 25 26 27 Priors: Silent or active partners of Bayesian inference? Samantha Low Choy 30 3.1 30 32 Priors in the very beginning 3.1.1 Priors as a basis for learning vi CONTENTS 3.1.2 Priors and philosophy 3.1.3 Prior chronology 3.1.4 Pooling prior information 3.2 Methodology I: Priors defined by mathematical criteria 3.2.1 Conjugate priors 3.2.2 Impropriety and hierarchical priors 3.2.3 Zellner’s g-prior for regression models 3.2.4 Objective priors 3.3 Methodology II: Modelling informative priors 3.3.1 Informative modelling approaches 3.3.2 Elicitation of distributions 3.4 Case studies 3.4.1 Normal likelihood: Time to submit research dissertations 3.4.2 Binomial likelihood: Surveillance for exotic plant pests 3.4.3 Mixture model likelihood: Bioregionalization 3.4.4 Logistic regression likelihood: Mapping species distribution via habitat models 3.5 Discussion 3.5.1 Limitations 3.5.2 Finding out about the problem 3.5.3 Prior formulation 3.5.4 Communication 3.5.5 Conclusion Acknowledgements References 32 33 34 35 35 37 37 38 40 40 42 44 Bayesian analysis of the normal linear regression model Christopher M Strickland and Clair L Alston 66 4.1 4.2 66 67 67 Introduction Case studies 4.2.1 Case study 1: Boston housing data set 4.2.2 Case study 2: Production of cars and station wagons 4.3 Matrix notation and the likelihood 4.4 Posterior inference 4.4.1 Natural conjugate prior 4.4.2 Alternative prior specifications 4.4.3 Generalizations of the normal linear model 4.4.4 Variable selection 4.5 Analysis 4.5.1 Case study 1: Boston housing data set 4.5.2 Case study 2: Car production data set References 44 47 50 53 57 57 58 59 60 61 61 61 67 67 68 69 73 74 78 81 81 85 88 CONTENTS Adapting ICU mortality models for local data: A Bayesian approach Petra L Graham, Kerrie L Mengersen and David A Cook 5.1 5.2 Introduction Case study: Updating a known risk-adjustment model for local use 5.3 Models and methods 5.4 Data analysis and results 5.4.1 Updating using the training data 5.4.2 Updating the model yearly 5.5 Discussion References A Bayesian regression model with variable selection for genome-wide association studies Carla Chen, Kerrie L Mengersen, Katja Ickstadt and Jonathan M Keith vii 90 90 91 92 96 96 98 100 101 103 6.1 6.2 6.3 6.4 Introduction Case study: Case–control of Type diabetes Case study: GENICA Models and methods 6.4.1 Main effect models 6.4.2 Main effects and interactions 6.5 Data analysis and results 6.5.1 WTCCC TID 6.5.2 GENICA 6.6 Discussion Acknowledgements References 6.A Appendix: SNP IDs 103 104 105 105 105 108 109 109 110 112 115 115 117 Bayesian meta-analysis Jegar O Pitchforth and Kerrie L Mengersen 118 7.1 7.2 118 7.3 Introduction Case study 1: Association between red meat consumption and breast cancer 7.2.1 Background 7.2.2 Meta-analysis models 7.2.3 Computation 7.2.4 Results 7.2.5 Discussion Case study 2: Trends in fish growth rate and size 7.3.1 Background 119 119 121 125 125 129 130 130 viii CONTENTS 7.3.2 Meta-analysis models 7.3.3 Computation 7.3.4 Results 7.3.5 Discussion Acknowledgements References 131 134 134 135 137 138 Bayesian mixed effects models Clair L Alston, Christopher M Strickland, Kerrie L Mengersen and Graham E Gardner 141 8.1 8.2 141 142 Introduction Case studies 8.2.1 Case study 1: Hot carcase weight of sheep carcases 8.2.2 Case study 2: Growth of primary school girls 8.3 Models and methods 8.3.1 Model for Case study 8.3.2 Model for Case study 8.3.3 MCMC estimation 8.4 Data analysis and results 8.5 Discussion References Ordering of hierarchies in hierarchical models: Bone mineral density estimation Cathal D Walsh and Kerrie L Mengersen 9.1 9.2 Introduction Case study 9.2.1 Measurement of bone mineral density 9.3 Models 9.3.1 Hierarchical model 9.3.2 Model H1 9.3.3 Model H2 9.4 Data analysis and results 9.4.1 Model H1 9.4.2 Model H2 9.4.3 Implication of ordering 9.4.4 Simulation study 9.4.5 Study design 9.4.6 Simulation study results 9.5 Discussion References 9.A Appendix: Likelihoods 142 142 146 147 148 149 150 158 158 159 159 160 160 161 162 163 163 164 164 165 166 166 166 167 168 168 170 CONTENTS ix 10 Bayesian Weibull survival model for gene expression data Sri Astuti Thamrin, James M McGree and Kerrie L Mengersen 171 10.1 Introduction 10.2 Survival analysis 10.3 Bayesian inference for the Weibull survival model 10.3.1 Weibull model without covariates 10.3.2 Weibull model with covariates 10.3.3 Model evaluation and comparison 10.4 Case study 10.4.1 Weibull model without covariates 10.4.2 Weibull survival model with covariates 10.4.3 Model evaluation and comparison 10.5 Discussion References 171 172 174 174 175 176 178 178 180 182 182 183 11 Bayesian change point detection in monitoring clinical outcomes Hassan Assareh, Ian Smith and Kerrie L Mengersen 11.1 11.2 11.3 11.4 11.5 11.6 11.7 Introduction Case study: Monitoring intensive care unit outcomes Risk-adjusted control charts Change point model Evaluation Performance analysis Comparison of Bayesian estimator with other methods 11.8 Conclusion References 12 Bayesian splines Samuel Clifford and Samantha Low Choy 12.1 Introduction 12.2 Models and methods 12.2.1 Splines and linear models 12.2.2 Link functions 12.2.3 Bayesian splines 12.2.4 Markov chain Monte Carlo 12.2.5 Model choice 12.2.6 Posterior diagnostics 12.3 Case studies 12.3.1 Data 12.3.2 Analysis 186 186 187 187 188 189 190 194 194 195 197 197 197 197 198 198 204 206 207 207 207 208 Index action potentials, 310–329 adaptive design, 361–373 adaptive MCMC, 22, 206, 209–211, 418 adaptive random walk, 205 adaptive SMC, 383–384 adjusted BIC, 297, 300 Akaike Information Criterion (AIC), 114, 206, 300 approximate Bayesian computation (ABC), 24–25, 374–375, 382–384 autoregressive regression, 446–449 basis function, 198–218, 244–245 Bayes factor, 61, 183, 206, 297, 300, 327 Bayesian hierarchical model, 17, 32, 34, 36, 107, 118, 123, 133, 137, 141, 159–169, 172–175, 221–237 Bayesian Information Criterion (BIC), 176–177, 182, 206, 260–262, 277, 425, 435 Bayesian lasso, 66, 78, 80–86 Bayesian network (BN), 348–360 Bayesian optimal design, 361–373 Bernoulli, 94, 107, 183, 187–190, 211, 216, 291, 298, 300, 355, 363 Bernoulli prior, 78 Besag, York and Mollie (BYM) model, 240 beta distribution, 36, 42, 45, 56, 318 birth death move, 26, 217, 337, 338, 341 Brook’s lemma, 244 B-spline, 177, 200–204, 208–210, 213–215, 217–218 capital asset pricing model (CAPM), 253 change point model, 19, 186–197, 208, 215, 293 Cholesky decomposition, 77, 258, 447 class membership, 293–295, 306 classification, 270, 292, 299, 330–347 classification and regression trees (CART), 330–347 clustering, 172, 223, 268, 273, 287, 290–291, 297, 396 CODA, 164, 190, 247, 423, 426 compartment model, 376 compiled languages, 422, 452 complexity, 27, 177, 241, 262, 292, 296, 367, 379, 389, 396, 422 conditional autoregressive (CAR), 225, 232, 240, 346 conditional probability table, 56, 349 confusion matrix, 339 control chart, 186–196 convergence, 20–22, 26, 81, 94, 125, 134, 164, 178, 205–207, 233–236, 265, 296, 404, 422 Case Studies in Bayesian Statistical Modelling and Analysis, First Edition Edited by Clair L Alston, Kerrie L Mengersen and Anthony N Pettitt © 2013 John Wiley & Sons, Ltd Published 2013 by John Wiley & Sons, Ltd 462 INDEX core process, 349, 350 cost effectiveness, 417 covariates, 37, 66, 105, 118, 125, 132–137, 175–176 Cox proportional hazards model, 177, 183 cubic regression spline, 200, 217 degeneracy, 383, 404, 409, 411 delayed rejection algorithm, 404–418 density estimation, 159–170, 267–286 deviance information criteria (DIC), 134, 151, 158, 165, 177, 182, 206, 230, 232, 244–245 diagnostics, 20, 95, 125, 207, 232, 337, 339–340 diffuse prior, 40, 175, 203 directed acyclic graph (DAG), 17, 348 direct elicitation, 41, 50, 60 Dirichlet process mixtures, 223 disease mapping, 221–239 dynamic linear model, 223 EM algorithm, 17, 267 ESS, 20–21, 362, 365–370, 412–416 prior elicitation, 33–34, 41–45, 50, 54–56, 60, 261 experts, 33–34, 41–61, 100, 331, 337, 340, 349, 352, 364 excess mortality, 229, 234, 236 exchangeable, 123, 168 exponentially weighted moving average (EWMA), 186–195 factor models, 223, 253 factorial hidden Markov model (HMM), 313, 315–317 false negative rate, 48, 331, 340 Fama–French model, 252–266 filtering, 257–259, 317 finite mixture models, 31, 267–280, 288–292, 311, 388–400 first order serial correlation, 75–76, 85, 87 Fortran, 422, 452, 455–456, 459 forward–backward algorithm, 317–318 Gaussian Markov random field (GMRF), 240 Gelman–Rubin statistic, 125, 179, 230, 295, 302 generalized linear mixed model (GLMM), 198, 204, 222–223 generalized linear model (GLM), 56, 101, 197, 208, 222, 362, 388 generalized additive model (GAM), 197–198 GeoBUGS, 246 GeoDa, 228 Geweke diagnostics, 232 Gibbs sampling, 17–19, 79, 273–275, 298, 318–319, 437 Gillespie’s algorithm, 380 goodness-of-fit, 95, 176–177, 206, 232, 296–297, 334, 357 graphical model, 348 growth mixture models, 292–295 hazard function, 172–174 hidden Markov model (HMM), 310–329 hierarchical model, 17, 34, 134–137, 162–163, 166, 168, 175–176, 223–225, 232–234, 268 hierarchical prior, 36–38, 50, 52–53, 80, 316 hierarchical random effects, 163 hybrid algorithm, 403–420 hyperparameter, 33, 38, 41, 177–178, 233, 276–277, 389, 394 importance distribution, 364–365 importance sampling, 23–24, 75, 363–368, 404, 407 importance weights, 364–365, 367, 404, 407, 411 impurity function, 334 incremental weights, 365 indirect elicitation, 41 informative priors, 40–41 intrinsic CAR prior, 226, 240 inverse gamma distribution, 69–70 iterative process, 349–351, 382–383 Jeffreys’ prior, 38–39, 48–49, 66, 73, 433 Kullback–Leibler divergence, 298, 392 label switching, 297–298, 323 Langevin algorithm, 404–405 INDEX latent class analysis, 268, 288 latent class model, 287, 309 latent growth process, 392–393 latent variable, 17, 50, 104, 279, 287, 289, 291, 294, 297, 311, 392–393 likelihood-free inference, 374–387 linear mixed effects model (LMM), 141–158, 198 linear regression, 36–38, 66–89, 132, 198–199, 208–209, 433, 437 logistic regression, 53–54, 91–101, 106, 211, 294–295, 363–364 Markov chain Monte Carlo (MCMC), 17–29 Markov process, 378–380 Markov random field (MRF), 274–275, 388 MATLAB, 208, 298, 339, 395, 412 matrix exponential, 378–380 meta-analysis, 118–140 Metroplis–Hastings algorthim (MH), 20–21, 75–77, 205, 404–407, 423, 427–429 Metropolis adjusted Langevin algorithm (MALA), 404, 407–408 misclassification rate, 340 mixed effects, 141–158, 198 mixed model, 137, 141–158 mixed treatment comparisons, 159 mixture model, 50–53, 267–286, 290–296, 388–400 mixture prior, 223 model choice, 206–207, 390 multimodal problem, 40, 383, 404, 413, 417 multinomial logistic regression, 295 multivariate random walk, 255–256 mutation step, 362, 382 mutual information, 351, 355 463 orientational bias Monte Carlo (OBMC), 429–430 piecewise polynomial spline, 293 pinball sampler, 404, 411 Poisson regression, 216–217 population Monte Carlo (PMC), 410–412 posterior predictive check, 95, 177, 331 posterior probabilities, 25, 71, 107, 256, 322, 335, 423 Potts model, 22, 274–275 prior distributions, 30–65, 69, 106, 273 prior elicitation, 30–65, 261 proportional hazards, 171, 236 pseudolikelihood, 24, 275 PyMCMC, 421–460 Python, 421–460 R2WinBUGS, 164, 189, 299 random walk proposal, 20, 22, 205, 383 rate of convergence, 417 relative excess risk (RER), 229–230, 234 repulsive proposal, 405–407, 410 reversible jump MCMC (RJMCMC), 25–26 risk-adjusted, 186–188 ROC, 95 roughness penalty, 202 neighbourhood matrices, 227–229, 236, 243, 246 nested structure, 162 noninformative prior, 38 sequential design, 361–367 sequential Monte Carlo (SMC), 361–373 slice sampler, 106, 108, 431–433 spatial correlation, 227, 243–247 spatial mixture model, 274–275, 278–279 spatial prior, 223 spatial smoothing, 223, 244 spatially structured random effects, 223, 227, 230 spike detection, 311 state space model, 252–266 stochastic search, 78–80, 107, 337–338, 437 subjective prior, 33 objective prior, 32–33, 38–39, 48–49 odds ratio, 121–123, 187–189 optimal design, 361–373 tempering, 21, 408–409 time series, 74–75, 132–134, 252–266 time varying regressors, 252–266 464 INDEX Toeplitz block circulation matrix, 204 transition matrix, 255, 313, 317, 327, 378 variable selection, 78–80, 103–117, 437 variational Bayes, 388–400 uniform prior, 30–31, 39 unsupervised detection, 311 unsupervised spatial clustering, 223, 291 utility function, 362, 367–368 Weibull model, 171–185 Wishart priors, 58, 147, 257, 260, 401 Zellner’s g-prior, 37–38, 69, 73, 434 WILEY SERIES IN PROBABILITY AND STATISTICS ESTABLISHED BY WALTER A SHEWHART AND SAMUEL S WILKS Editors: David J Balding, Noel A.C Cressie, Garrett M Fitzmaurice, Harvey Goldstein, Iain M Johnstone, Geert Molenberghs, David W Scott, Adrian F M Smith, Ruey S Tsay, Sanford Weisberg Editors Emeriti: Vic Barnett, Ralph A Bradley, J Stuart Hunter, J.B Kadane, David G Kendall, Jozef L Teugels The Wiley Series in Probability and Statistics is well established and authoritative It covers many topics of current research interest in both pure and applied statistics and probability theory Written by leading statisticians and institutions, the titles span both state-of-the-art developments in the field and classical methods Reflecting the wide range of current research in statistics, the series encompasses applied, methodological and theoretical statistics, ranging from applications and new techniques made possible by advances in computerized practice to rigorous treatment of theoretical approaches This series provides essential and invaluable reading for all statisticians, whether in academia, industry, government, or research † ABRAHAM and LEDOLTER Á Statistical Methods for Forecasting AGRESTI Á Analysis of Ordinal Categorical Data, Second Edition AGRESTI Á An Introduction to Categorical Data Analysis, Second Edition AGRESTI Á Categorical Data Analysis, Second Edition ALSTON, MENGERSEN and PETTITT (editors) Á Case Studies in Bayesian Statistical Modelling and Analysis ALTMAN, GILL, and McDONALD Á Numerical Issues in Statistical Computing for the Social Scientist AMARATUNGA and CABRERA Á Exploration and Analysis of DNA Microarray and Protein Array Data ANDEˇL Á Mathematics of Chance ANDERSON Á An Introduction to Multivariate Statistical Analysis, Third Edition à ANDERSON Á The Statistical Analysis of Time Series ANDERSON, AUQUIER, HAUCK, OAKES, VANDAELE, and WEISBERG Á Statistical Methods for Comparative Studies ANDERSON and LOYNES Á The Teaching of Practical Statistics ARMITAGE and DAVID (editors) Á Advances in Biometry ARNOLD, BALAKRISHNAN, and NAGARAJA Á Records à ARTHANARI and DODGE Á Mathematical Programming in Statistics à BAILEY Á The Elements of Stochastic Processes with Applications to the Natural Sciences BAJORSKI Á Statistics for Imaging, Optics, and Photonics BALAKRISHNAN and KOUTRAS Á Runs and Scans with Applications BALAKRISHNAN and NG Á Precedence-Type Tests and Applications BARNETT Á Comparative Statistical Inference, Third Edition BARNETT Á Environmental Statistics BARNETT and LEWIS Á Outliers in Statistical Data, Third Edition BARTHOLOMEW, KNOTT, and MOUSTAKI Á Latent Variable Models and Factor Analysis: A Unified Approach, Third Edition à Now available in a lower priced paperback edition in the Wiley Classics Library † Now available in a lower priced paperback edition in the Wiley-Interscience Paperback Series BARTOSZYNSKI and NIEWIADOMSKA-BUGAJ Á Probability and Statistical Inference, Second Edition BASILEVSKY Á Statistical Factor Analysis and Related Methods: Theory and Applications BATES and WATTS Á Nonlinear Regression Analysis and Its Applications BECHHOFER, SANTNER, and GOLDSMAN Á Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons BEIRLANT, GOEGEBEUR, SEGERS, TEUGELS, and DE WAAL Á Statistics of Extremes: Theory and Applications BELSLEY Á Conditioning Diagnostics: Collinearity and Weak Data in Regression † BELSLEY, KUH, and WELSCH Á Regression Diagnostics: Identifying Influential Data and Sources of Collinearity BENDAT and PIERSOL Á Random Data: Analysis and Measurement Procedures, Fourth Edition BERNARDO and SMITH Á Bayesian Theory BHAT and MILLER Á Elements of Applied Stochastic Processes, Third Edition BHATTACHARYA and WAYMIRE Á Stochastic Processes with Applications BIEMER, GROVES, LYBERG, MATHIOWETZ, and SUDMAN Á Measurement Errors in Surveys BILLINGSLEY Á Convergence of Probability Measures, Second Edition BILLINGSLEY Á Probability and Measure, Anniversary Edition BIRKES and DODGE Á Alternative Methods of Regression BISGAARD and KULAHCI Á Time Series Analysis and Forecasting by Example BISWAS, DATTA, FINE, and SEGAL Á Statistical Advances in the Biomedical Sciences: Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics BLISCHKE and MURTHY (editors) Á Case Studies in Reliability and Maintenance BLISCHKE and MURTHY Á Reliability: Modeling, Prediction, and Optimization BLOOMFIELD Á Fourier Analysis of Time Series: An Introduction, Second Edition BOLLEN Á Structural Equations with Latent Variables BOLLEN and CURRAN Á Latent Curve Models: A Structural Equation Perspective BOROVKOV Á Ergodicity and Stability of Stochastic Processes BOSQ and BLANKE Á Inference and Prediction in Large Dimensions BOULEAU Á Numerical Methods for Stochastic Processes à BOX Á Bayesian Inference in Statistical Analysis BOX Á Improving Almost Anything, Revised Edition à BOX and DRAPER Á Evolutionary Operation: A Statistical Method for Process Improvement BOX and DRAPER Á Response Surfaces, Mixtures, and Ridge Analyses, Second Edition BOX, HUNTER, and HUNTER Á Statistics for Experimenters: Design, Innovation, and Discovery, Second Editon BOX, JENKINS, and REINSEL Á Time Series Analysis: Forcasting and Control, Fourth Edition ˜ O, and PANIAGUA-QUIN ˜ ONES Á Statistical Control by Monitoring and BOX, LUCEN Adjustment, Second Edition à BROWN and HOLLANDER Á Statistics: A Biomedical Introduction CAIROLI and DALANG Á Sequential Stochastic Optimization à Now available in a lower priced paperback edition in the Wiley Classics Library † Now available in a lower priced paperback edition in the Wiley-Interscience Paperback Series à à à CASTILLO, HADI, BALAKRISHNAN, and SARABIA Á Extreme Value and Related Models with Applications in Engineering and Science CHAN Á Time Series: Applications to Finance with R and S-PlusÒ , Second Edition CHARALAMBIDES Á Combinatorial Methods in Discrete Distributions CHATTERJEE and HADI Á Regression Analysis by Example, Fourth Edition CHATTERJEE and HADI Á Sensitivity Analysis in Linear Regression CHERNICK Á Bootstrap Methods: A Guide for Practitioners and Researchers, Second Edition CHERNICK and FRIIS Á Introductory Biostatistics for the Health Sciences CHILES and DELFINER Á Geostatistics: Modeling Spatial Uncertainty, Second Edition CHOW and LIU Á Design and Analysis of Clinical Trials: Concepts and Methodologies, Second Edition CLARKE Á Linear Models: The Theory and Application of Analysis of Variance CLARKE and DISNEY Á Probability and Random Processes: A First Course with Applications, Second Edition COCHRAN and COX Á Experimental Designs, Second Edition COLLINS and LANZA Á Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences CONGDON Á Applied Bayesian Modelling CONGDON Á Bayesian Models for Categorical Data CONGDON Á Bayesian Statistical Modelling, Second Edition CONOVER Á Practical Nonparametric Statistics, Third Edition COOK Á Regression Graphics COOK and WEISBERG Á An Introduction to Regression Graphics COOK and WEISBERG Á Applied Regression Including Computing and Graphics CORNELL Á A Primer on Experiments with Mixtures CORNELL Á Experiments with Mixtures, Designs, Models, and the Analysis of Mixture Data, Third Edition COX Á A Handbook of Introductory Statistical Methods CRESSIE Á Statistics for Spatial Data, Revised Edition CRESSIE and WIKLE Á Statistics for Spatio-Temporal Data € O } and HORVATH  CSORG Á Limit Theorems in Change Point Analysis DAGPUNAR Á Simulation and Monte Carlo: With Applications in Finance and MCMC DANIEL Á Applications of Statistics to Industrial Experimentation DANIEL Á Biostatistics: A Foundation for Analysis in the Health Sciences, Eighth Edition DANIEL Á Fitting Equations to Data: Computer Analysis of Multifactor Data, Second Edition DASU and JOHNSON Á Exploratory Data Mining and Data Cleaning DAVID and NAGARAJA Á Order Statistics, Third Edition DEGROOT, FIENBERG, and KADANE Á Statistics and the Law DEL CASTILLO Á Statistical Process Adjustment for Quality Control DEMARIS Á Regression with Social Data: Modeling Continuous and Limited Response Variables DEMIDENKO Á Mixed Models: Theory and Applications à Now available in a lower priced paperback edition in the Wiley Classics Library † Now available in a lower priced paperback edition in the Wiley-Interscience Paperback Series DENISON, HOLMES, MALLICK and SMITH Á Bayesian Methods for Nonlinear Classification and Regression DETTE and STUDDEN Á The Theory of Canonical Moments with Applications in Statistics, Probability, and Analysis DEY and MUKERJEE Á Fractional Factorial Plans DE ROCQUIGNY Á Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Models DILLON and GOLDSTEIN Á Multivariate Analysis: Methods and Applications à DODGE and ROMIG Á Sampling Inspection Tables, Second Edition à DOOB Á Stochastic Processes DOWDY, WEARDEN, and CHILKO Á Statistics for Research, Third Edition DRAPER and SMITH Á Applied Regression Analysis, Third Edition DRYDEN and MARDIA Á Statistical Shape Analysis DUDEWICZ and MISHRA Á Modern Mathematical Statistics DUNN and CLARK Á Basic Statistics: A Primer for the Biomedical Sciences, Fourth Edition DUPUIS and ELLIS Á A Weak Convergence Approach to the Theory of Large Deviations EDLER and KITSOS Á Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment à ELANDT-JOHNSON and JOHNSON Á Survival Models and Data Analysis ENDERS Á Applied Econometric Time Series, Third Edition † ETHIER and KURTZ Á Markov Processes: Characterization and Convergence EVANS, HASTINGS, and PEACOCK Á Statistical Distributions, Third Edition EVERITT, LANDAU, LEESE, and STAHL Á Cluster Analysis, Fifth Edition FEDERER and KING Á Variations on Split Plot and Split Block Experiment Designs FELLER Á An Introduction to Probability Theory and Its Applications, Volume I, Third Edition, Revised; Volume II, Second Edition FITZMAURICE, LAIRD, and WARE Á Applied Longitudinal Analysis, Second Edition à FLEISS Á The Design and Analysis of Clinical Experiments FLEISS Á Statistical Methods for Rates and Proportions, Third Edition † FLEMING and HARRINGTON Á Counting Processes and Survival Analysis FUJIKOSHI, ULYANOV, and SHIMIZU Á Multivariate Statistics: High-Dimensional and Large-Sample Approximations FULLER Á Introduction to Statistical Time Series, Second Edition † FULLER Á Measurement Error Models GALLANT Á Nonlinear Statistical Models GEISSER Á Modes of Parametric Statistical Inference GELMAN and MENG Á Applied Bayesian Modeling and Causal Inference from IncompleteData Perspectives GEWEKE Á Contemporary Bayesian Econometrics and Statistics GHOSH, MUKHOPADHYAY, and SEN Á Sequential Estimation GIESBRECHT and GUMPERTZ Á Planning, Construction, and Statistical Analysis of Comparative Experiments GIFI Á Nonlinear Multivariate Analysis GIVENS and HOETING Á Computational Statistics à Now available in a lower priced paperback edition in the Wiley Classics Library † Now available in a lower priced paperback edition in the Wiley-Interscience Paperback Series GLASSERMAN and YAO Á Monotone Structure in Discrete-Event Systems GNANADESIKAN Á Methods for Statistical Data Analysis of Multivariate Observations, Second Edition GOLDSTEIN Á Multilevel Statistical Models, Fourth Edition GOLDSTEIN and LEWIS Á Assessment: Problems, Development, and Statistical Issues GOLDSTEIN and WOOFF Á Bayes Linear Statistics GREENWOOD and NIKULIN Á A Guide to Chi-Squared Testing GROSS, SHORTLE, THOMPSON, and HARRIS Á Fundamentals of Queueing Theory, Fourth Edition GROSS, SHORTLE, THOMPSON, and HARRIS Á Solutions Manual to Accompany Fundamentals of Queueing Theory, Fourth Edition à HAHN and SHAPIRO Á Statistical Models in Engineering HAHN and MEEKER Á Statistical Intervals: A Guide for Practitioners HALD Á A History of Probability and Statistics and their Applications Before 1750 † HAMPEL Á Robust Statistics: The Approach Based on Influence Functions HARTUNG, KNAPP, and SINHA Á Statistical Meta-Analysis with Applications HEIBERGER Á Computation for the Analysis of Designed Experiments HEDAYAT and SINHA Á Design and Inference in Finite Population Sampling HEDEKER and GIBBONS Á Longitudinal Data Analysis HELLER Á MACSYMA for Statisticians HERITIER, CANTONI, COPT, and VICTORIA-FESER Á Robust Methods in Biostatistics HINKELMANN and KEMPTHORNE Á Design and Analysis of Experiments, Volume 1: Introduction to Experimental Design, Second Edition HINKELMANN and KEMPTHORNE Á Design and Analysis of Experiments, Volume 2: Advanced Experimental Design HINKELMANN (editor) Á Design and Analysis of Experiments, Volume 3: Special Designs and Applications à HOAGLIN, MOSTELLER, and TUKEY Á Fundamentals of Exploratory Analysis of Variance à HOAGLIN, MOSTELLER, and TUKEY Á Exploring Data Tables, Trends and Shapes à HOAGLIN, MOSTELLER, and TUKEY Á Understanding Robust and Exploratory Data Analysis HOCHBERG and TAMHANE Á Multiple Comparison Procedures HOCKING Á Methods and Applications of Linear Models: Regression and the Analysis of Variance, Second Edition HOEL Á Introduction to Mathematical Statistics, Fifth Edition HOGG and KLUGMAN Á Loss Distributions HOLLANDER and WOLFE Á Nonparametric Statistical Methods, Second Edition HOSMER and LEMESHOW Á Applied Logistic Regression, Second Edition HOSMER, LEMESHOW, and MAY Á Applied Survival Analysis: Regression Modeling of Time-to-Event Data, Second Edition HUBER Á Data Analysis: What Can Be Learned From the Past 50 Years HUBER Á Robust Statistics † HUBER and RONCHETTI Á Robust Statistics, Second Edition HUBERTY Á Applied Discriminant Analysis, Second Edition à Now available in a lower priced paperback edition in the Wiley Classics Library † Now available in a lower priced paperback edition in the Wiley-Interscience Paperback Series HUBERTY and OLEJNIK Á Applied MANOVA and Discriminant Analysis, Second Edition HUITEMA Á The Analysis of Covariance and Alternatives: Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies, Second Edition HUNT and KENNEDY Á Financial Derivatives in Theory and Practice, Revised Edition HURD and MIAMEE Á Periodically Correlated Random Sequences: Spectral Theory and Practice HUSKOVA, BERAN, and DUPAC Á Collected Works of Jaroslav Hajek—with Commentary HUZURBAZAR Á Flowgraph Models for Multistate Time-to-Event Data INSUA, RUGGERI and WIPER Á Bayesian Analysis of Stochastic Process Models JACKMAN Á Bayesian Analysis for the Social Sciences † JACKSON Á A User’s Guide to Principle Components JOHN Á Statistical Methods in Engineering and Quality Assurance JOHNSON Á Multivariate Statistical Simulation JOHNSON and BALAKRISHNAN Á Advances in the Theory and Practice of Statistics: A Volume in Honor of Samuel Kotz JOHNSON, KEMP, and KOTZ Á Univariate Discrete Distributions, Third Edition JOHNSON and KOTZ (editors) Á Leading Personalities in Statistical Sciences: From the Seventeenth Century to the Present JOHNSON, KOTZ, and BALAKRISHNAN Á Continuous Univariate Distributions, Volume 1, Second Edition JOHNSON, KOTZ, and BALAKRISHNAN Á Continuous Univariate Distributions, Volume 2, Second Edition JOHNSON, KOTZ, and BALAKRISHNAN Á Discrete Multivariate Distributions € JUDGE, GRIFFITHS, HILL, LUTKEPOHL, and LEE Á The Theory and Practice of Econometrics, Second Edition JUREK and MASON Á Operator-Limit Distributions in Probability Theory KADANE Á Bayesian Methods and Ethics in a Clinical Trial Design KADANE AND SCHUM Á A Probabilistic Analysis of the Sacco and Vanzetti Evidence KALBFLEISCH and PRENTICE Á The Statistical Analysis of Failure Time Data, Second Edition KARIYA and KURATA Á Generalized Least Squares KASS and VOS Á Geometrical Foundations of Asymptotic Inference † KAUFMAN and ROUSSEEUW Á Finding Groups in Data: An Introduction to Cluster Analysis KEDEM and FOKIANOS Á Regression Models for Time Series Analysis KENDALL, BARDEN, CARNE, and LE Á Shape and Shape Theory KHURI Á Advanced Calculus with Applications in Statistics, Second Edition KHURI, MATHEW, and SINHA Á Statistical Tests for Mixed Linear Models à KISH Á Statistical Design for Research KLEIBER and KOTZ Á Statistical Size Distributions in Economics and Actuarial Sciences € Á Smoothing of Multivariate Data: Density Estimation and Visualization KLEMELA KLUGMAN, PANJER, and WILLMOT Á Loss Models: From Data to Decisions, Third Edition KLUGMAN, PANJER, and WILLMOT Á Solutions Manual to Accompany Loss Models: From Data to Decisions, Third Edition à Now available in a lower priced paperback edition in the Wiley Classics Library † Now available in a lower priced paperback edition in the Wiley-Interscience Paperback Series KOSKI and NOBLE Á Bayesian Networks: An Introduction KOTZ, BALAKRISHNAN, and JOHNSON Á Continuous Multivariate Distributions, Volume 1, Second Edition KOTZ and JOHNSON (editors) Á Encyclopedia of Statistical Sciences: Volumes to with Index KOTZ and JOHNSON (editors) Á Encyclopedia of Statistical Sciences: Supplement Volume KOTZ, READ, and BANKS (editors) Á Encyclopedia of Statistical Sciences: Update Volume KOTZ, READ, and BANKS (editors) Á Encyclopedia of Statistical Sciences: Update Volume KOWALSKI and TU Á Modern Applied U-Statistics KRISHNAMOORTHY and MATHEW Á Statistical Tolerance Regions: Theory, Applications, and Computation KROESE, TAIMRE, and BOTEV Á Handbook of Monte Carlo Methods KROONENBERG Á Applied Multiway Data Analysis KULINSKAYA, MORGENTHALER, and STAUDTE Á Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence KULKARNI and HARMAN Á An Elementary Introduction to Statistical Learning Theory KUROWICKA and COOKE Á Uncertainty Analysis with High Dimensional Dependence Modelling KVAM and VIDAKOVIC Á Nonparametric Statistics with Applications to Science and Engineering LACHIN Á Biostatistical Methods: The Assessment of Relative Risks, Second Edition LAD Á Operational Subjective Statistical Methods: A Mathematical, Philosophical, and Historical Introduction LAMPERTI Á Probability: A Survey of the Mathematical Theory, Second Edition LAWLESS Á Statistical Models and Methods for Lifetime Data, Second Edition LAWSON Á Statistical Methods in Spatial Epidemiology, Second Edition LE Á Applied Categorical Data Analysis, Second Edition LE Á Applied Survival Analysis LEE Á Structural Equation Modeling: A Bayesian Approach LEE and WANG Á Statistical Methods for Survival Data Analysis, Third Edition LEPAGE and BILLARD Á Exploring the Limits of Bootstrap LESSLER and KALSBEEK Á Nonsampling Errors in Surveys LEYLAND and GOLDSTEIN (editors) Á Multilevel Modelling of Health Statistics LIAO Á Statistical Group Comparison LIN Á Introductory Stochastic Analysis for Finance and Insurance LITTLE and RUBIN Á Statistical Analysis with Missing Data, Second Edition LLOYD Á The Statistical Analysis of Categorical Data LOWEN and TEICH Á Fractal-Based Point Processes MAGNUS and NEUDECKER Á Matrix Differential Calculus with Applications in Statistics and Econometrics, Revised Edition MALLER and ZHOU Á Survival Analysis with Long Term Survivors MARCHETTE Á Random Graphs for Statistical Pattern Recognition MARDIA and JUPP Á Directional Statistics à Now available in a lower priced paperback edition in the Wiley Classics Library † Now available in a lower priced paperback edition in the Wiley-Interscience Paperback Series MARKOVICH Á Nonparametric Analysis of Univariate Heavy-Tailed Data: Research and Practice MARONNA, MARTIN and YOHAI Á Robust Statistics: Theory and Methods MASON, GUNST, and HESS Á Statistical Design and Analysis of Experiments with Applications to Engineering and Science, Second Edition McCULLOCH, SEARLE, and NEUHAUS Á Generalized, Linear, and Mixed Models, Second Edition McFADDEN Á Management of Data in Clinical Trials, Second Edition à McLACHLAN Á Discriminant Analysis and Statistical Pattern Recognition McLACHLAN, DO, and AMBROISE Á Analyzing Microarray Gene Expression Data McLACHLAN and KRISHNAN Á The EM Algorithm and Extensions, Second Edition McLACHLAN and PEEL Á Finite Mixture Models McNEIL Á Epidemiological Research Methods MEEKER and ESCOBAR Á Statistical Methods for Reliability Data MEERSCHAERT and SCHEFFLER Á Limit Distributions for Sums of Independent Random Vectors: Heavy Tails in Theory and Practice MENGERSEN, ROBERT, and TITTERINGTON Á Mixtures: Estimation and Applications MICKEY, DUNN, and CLARK Á Applied Statistics: Analysis of Variance and Regression, Third Edition à MILLER Á Survival Analysis, Second Edition MONTGOMERY, JENNINGS, and KULAHCI Á Introduction to Time Series Analysis and Forecasting MONTGOMERY, PECK, and VINING Á Introduction to Linear Regression Analysis, Fifth Edition MORGENTHALER and TUKEY Á Configural Polysampling: A Route to Practical Robustness MUIRHEAD Á Aspects of Multivariate Statistical Theory MULLER and STOYAN Á Comparison Methods for Stochastic Models and Risks MURTHY, XIE, and JIANG Á Weibull Models MYERS, MONTGOMERY, and ANDERSON-COOK Á Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Third Edition MYERS, MONTGOMERY, VINING, and ROBINSON Á Generalized Linear Models With Applications in Engineering and the Sciences, Second Edition NATVIG Á Multistate Systems Reliability Theory With Applications † NELSON Á Accelerated Testing, Statistical Models, Test Plans, and Data Analyses † NELSON Á Applied Life Data Analysis NEWMAN Á Biostatistical Methods in Epidemiology NG, TAIN, and TANG Á Dirichlet Theory: Theory, Methods and Applications OKABE, BOOTS, SUGIHARA, and CHIU Á Spatial Tesselations: Concepts and Applications of Voronoi Diagrams, Second Edition OLIVER and SMITH Á Influence Diagrams, Belief Nets and Decision Analysis PALTA Á Quantitative Methods in Population Health: Extensions of Ordinary Regressions PANJER Á Operational Risk: Modeling and Analytics PANKRATZ Á Forecasting with Dynamic Regression Models PANKRATZ Á Forecasting with Univariate Box-Jenkins Models: Concepts and Cases à Now available in a lower priced paperback edition in the Wiley Classics Library † Now available in a lower priced paperback edition in the Wiley-Interscience Paperback Series PARDOUX Á Markov Processes and Applications: Algorithms, Networks, Genome and Finance PARMIGIANI and INOUE Á Decision Theory: Principles and Approaches à PARZEN Á Modern Probability Theory and Its Applications ˜ A, TIAO, and TSAY Á A Course in Time Series Analysis PEN PESARIN and SALMASO Á Permutation Tests for Complex Data: Applications and Software PIANTADOSI Á Clinical Trials: A Methodologic Perspective, Second Edition POURAHMADI Á Foundations of Time Series Analysis and Prediction Theory POWELL Á Approximate Dynamic Programming: Solving the Curses of Dimensionality, Second Edition POWELL and RYZHOV Á Optimal Learning PRESS Á Subjective and Objective Bayesian Statistics, Second Edition PRESS and TANUR Á The Subjectivity of Scientists and the Bayesian Approach PURI, VILAPLANA, and WERTZ Á New Perspectives in Theoretical and Applied Statistics † PUTERMAN Á Markov Decision Processes: Discrete Stochastic Dynamic Programming QIU Á Image Processing and Jump Regression Analysis à RAO Á Linear Statistical Inference and Its Applications, Second Edition RAO Á Statistical Inference for Fractional Diffusion Processes RAUSAND and HØYLAND Á System Reliability Theory: Models, Statistical Methods, and Applications, Second Edition RAYNER, THAS, and BEST Á Smooth Tests of Goodnes of Fit: Using R, Second Edition RENCHER Á Linear Models in Statistics, Second Edition RENCHER Á Methods of Multivariate Analysis, Second Edition RENCHER Á Multivariate Statistical Inference with Applications RIGDON and BASU Á Statistical Methods for the Reliability of Repairable Systems à RIPLEY Á Spatial Statistics à RIPLEY Á Stochastic Simulation ROHATGI and SALEH Á An Introduction to Probability and Statistics, Second Edition ROLSKI, SCHMIDLI, SCHMIDT, and TEUGELS Á Stochastic Processes for Insurance and Finance ROSENBERGER and LACHIN Á Randomization in Clinical Trials: Theory and Practice ROSSI, ALLENBY, and McCULLOCH Á Bayesian Statistics and Marketing † ROUSSEEUW and LEROY Á Robust Regression and Outlier Detection ROYSTON and SAUERBREI Á Multivariate Model Building: A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modeling Continuous Variables à RUBIN Á Multiple Imputation for Nonresponse in Surveys RUBINSTEIN and KROESE Á Simulation and the Monte Carlo Method, Second Edition RUBINSTEIN and MELAMED Á Modern Simulation and Modeling RYAN Á Modern Engineering Statistics RYAN Á Modern Experimental Design RYAN Á Modern Regression Methods, Second Edition RYAN Á Statistical Methods for Quality Improvement, Third Edition SALEH Á Theory of Preliminary Test and Stein-Type Estimation with Applications à Now available in a lower priced paperback edition in the Wiley Classics Library † Now available in a lower priced paperback edition in the Wiley-Interscience Paperback Series à à † † † † † à SALTELLI, CHAN, and SCOTT (editors) Á Sensitivity Analysis SCHERER Á Batch Effects and Noise in Microarray Experiments: Sources and Solutions SCHEFFE Á The Analysis of Variance SCHIMEK Á Smoothing and Regression: Approaches, Computation, and Application SCHOTT Á Matrix Analysis for Statistics, Second Edition SCHOUTENS Á Levy Processes in Finance: Pricing Financial Derivatives SCOTT Á Multivariate Density Estimation: Theory, Practice, and Visualization SEARLE Á Linear Models SEARLE Á Linear Models for Unbalanced Data SEARLE Á Matrix Algebra Useful for Statistics SEARLE, CASELLA, and McCULLOCH Á Variance Components SEARLE and WILLETT Á Matrix Algebra for Applied Economics SEBER Á A Matrix Handbook For Statisticians SEBER Á Multivariate Observations SEBER and LEE Á Linear Regression Analysis, Second Edition SEBER and WILD Á Nonlinear Regression SENNOTT Á Stochastic Dynamic Programming and the Control of Queueing Systems SERFLING Á Approximation Theorems of Mathematical Statistics SHAFER and VOVK Á Probability and Finance: It’s Only a Game! SHERMAN Á Spatial Statistics and Spatio-Temporal Data: Covariance Functions and Directional Properties SILVAPULLE and SEN Á Constrained Statistical Inference: Inequality, Order, and Shape Restrictions SINGPURWALLA Á Reliability and Risk: A Bayesian Perspective SMALL and McLEISH Á Hilbert Space Methods in Probability and Statistical Inference SRIVASTAVA Á Methods of Multivariate Statistics STAPLETON Á Linear Statistical Models, Second Edition STAPLETON Á Models for Probability and Statistical Inference: Theory and Applications STAUDTE and SHEATHER Á Robust Estimation and Testing STOYAN Á Counterexamples in Probability, Second Edition STOYAN, KENDALL, and MECKE Á Stochastic Geometry and Its Applications, Second Edition STOYAN and STOYAN Á Fractals, Random Shapes and Point Fields: Methods of Geometrical Statistics STREET and BURGESS Á The Construction of Optimal Stated Choice Experiments: Theory and Methods STYAN Á The Collected Papers of T W Anderson: 1943–1985 SUTTON, ABRAMS, JONES, SHELDON, and SONG Á Methods for Meta-Analysis in Medical Research TAKEZAWA Á Introduction to Nonparametric Regression TAMHANE Á Statistical Analysis of Designed Experiments: Theory and Applications TANAKA Á Time Series Analysis: Nonstationary and Noninvertible Distribution Theory THOMPSON Á Empirical Model Building: Data, Models, and Reality, Second Edition THOMPSON Á Sampling, Third Edition THOMPSON Á Simulation: A Modeler’s Approach à Now available in a lower priced paperback edition in the Wiley Classics Library † Now available in a lower priced paperback edition in the Wiley-Interscience Paperback Series THOMPSON and SEBER Á Adaptive Sampling THOMPSON, WILLIAMS, and FINDLAY Á Models for Investors in Real World Markets TIERNEY Á LISP-STAT: An Object-Oriented Environment for Statistical Computing and Dynamic Graphics TSAY Á Analysis of Financial Time Series, Third Edition UPTON and FINGLETON Á Spatial Data Analysis by Example, Volume II: Categorical and Directional Data † VAN BELLE Á Statistical Rules of Thumb, Second Edition VAN BELLE, FISHER, HEAGERTY, and LUMLEY Á Biostatistics: A Methodology for the Health Sciences, Second Edition VESTRUP Á The Theory of Measures and Integration VIDAKOVIC Á Statistical Modeling by Wavelets VIERTL Á Statistical Methods for Fuzzy Data VINOD and REAGLE Á Preparing for the Worst: Incorporating Downside Risk in Stock Market Investments WALLER and GOTWAY Á Applied Spatial Statistics for Public Health Data WANG and WANG Á Structural Equation Modeling: Applications Using Mplus WEISBERG Á Applied Linear Regression, Third Edition WEISBERG Á Bias and Causation: Models and Judgment for Valid Comparisons WELSH Á Aspects of Statistical Inference WESTFALL and YOUNG Á Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment à WHITTAKER Á Graphical Models in Applied Multivariate Statistics WINKER Á Optimization Heuristics in Economics: Applications of Threshold Accepting WOOD WORTH Á Biostatistics: A Bayesian Introduction WOOLSON and CLARKE Á Statistical Methods for the Analysis of Biomedical Data, Second Edition WU and HAMADA Á Experiments: Planning, Analysis, and Parameter Design Optimization, Second Edition WU and ZHANG Á Nonparametric Regression Methods for Longitudinal Data Analysis YIN Á Clinical Trial Design: Bayesian and Frequentist Adaptive Methods YOUNG, VALERO-MORA, and FRIENDLY Á Visual Statistics: Seeing Data with Dynamic Interactive Graphics ZACKS Á Stage-Wise Adaptive Designs à ZELLNER Á An Introduction to Bayesian Inference in Econometrics ZELTERMAN Á Discrete Distributions—Applications in the Health Sciences ZHOU, OBUCHOWSKI, and McCLISH Á Statistical Methods in Diagnostic Medicine, Second Edition à Now available in a lower priced paperback edition in the Wiley Classics Library † Now available in a lower priced paperback edition in the Wiley-Interscience Paperback Series ... webs: Using Bayesian networks in the fight against infection Mary Waterhouse and Sandra Johnson 348 20.1 Introduction to Bayesian network modelling 20.1.1 Building a BN 20.2 Introduction to case. .. Cataloging -in- Publication Data Case studies in Bayesian statistical modelling and analysis / edited by Clair Alston, Kerrie Mengersen, and Anthony Pettitt pages cm Includes bibliographical references and index ISBN.. .Case Studies in Bayesian Statistical Modelling and Analysis WILEY SERIES IN PROBABILITY AND STATISTICS Established by WALTER A SHEWHART and SAMUEL S WILKS Editors David J Balding, Noel