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The goal of this book is to provide an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The coverage of statistical modeling using WinBUGS continues in the new edition although some examples using R and MLWIN are also introduced to broaden appeal and for completeness of coverage.
Preface. Chapter 1 Introduction: The Bayesian Method, its Benefits and Implementation. Chapter 2 Bayesian Model Choice, Comparison and Checking. Chapter 3 The Major Densities and their Application. Chapter 4 Normal Linear Regression, General Linear Models and Log Linear Models. Chapter 5 Hierarchical Priors for Pooling Strength and Overdispersed Regression Modelling. Chapter 6 Discrete Mixture Priors. Chapter 7 Multinomial and Ordinal Regression Models. Chapter 8 Time Series Models. Chapter 9 Modelling Spatial Dependencies. Chapter 10 Nonlinear and Nonparametric Regression. Chapter 11 Multilevel and Panel Data Models. Chapter 12 Latent Variable and Structural Equation Models for Multivariate Data. Chapter 13 Survival and Event History Analysis. Chapter 14 Missing Data Models. Chapter 15 Measurement Error, Seemingly Unrelated Regressions, and Simultaneous Equations. Appendix 1 A Brief Guide to Using WINBUGS. Index.
Peter Congdon is Research Professor of Quantitative Geography and Health Statistics at Queen Mary University of London. He has written three earlier books on Bayesian modelling and data analysis techniques with Wiley, and has a wide range of publications in statistical methodology and in application areas. His current interests include applications to spatial and survey data relating to health status and health service research. His recent publications include work associated with the British Historical GIS Project (University of Portsmouth) and international collaborative work on psychiatric admissions in London and New York.
This text is ideal for researchers in applied statistics, medical sciences, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. (Zentralblatt MATH, 2010)