Bayesian Hierarchical Models: With Applications Using R

✍ Scribed by Peter D. Congdon

• Publisher: Chapman & Hall/CRC Press/Taylor & Francis Group
• Year: 2020
• Tongue: English
• Leaves: 593
• Edition: 2nd Edition
• Category: Library

✦ Synopsis

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. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website.

✦ Table of Contents

ContentsPreface1. Bayesian Methods for Complex Data: Estimation and Inference2. Bayesian Analysis Options in R, and Coding for BUGS, JAGS, and Stan3. Model Fit, Comparison, and Checking4. Borrowing Strength via Hierarchical Estimation5. Time Structured Priors6. Representing Spatial Dependence7. Regression Techniques Using Hierarchical Priors8. Bayesian Multilevel Models9. Factor Analysis, Structural Equation Models, and Multivariate Priors10. Hierarchical Models for Longitudinal Data11. Survival and Event History Models12. Hierarchical Methods for Nonlinear and Quantile Regression

✦ Subjects

Multilevel Models [Statistics], Bayesian Statistical Decision Theory