Chapman and Hall/CRC
572 pages | 70 B/W Illus.
An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods.
The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples.
The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities.
Chapter 1 Bayesian Methods for Complex Data: Estimation and Inference; Chapter 2 Computing Options and Strategies; Chapter 3 Model Fit, Comparison, and Checking; Chapter 4 Borrowing Strength Estimation for Exchangeable Units; Chapter 5 Structured Priors Recognizing Similarity over Time and Space; Chapter 6 Regression Techniques using Hierarchical Priors; Chapter 7 Multilevel Models; Chapter 8 Regression for Causal Effects with Observational Data; Chapter 9 Hierarchical Models for Panel Data; Chapter 10 Multivariate Priors, with a Focus on Factor and Structural Equation Models; Chapter 11 Survival and Event History Models; Chapter 12 Hierarchical Methods for Nonlinear Regression.