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"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)
Table of Contents
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.
