2nd Edition
Bayesian Hierarchical Models With Applications Using R, Second Edition
Contents
Preface
1. Bayesian Methods for Complex Data: Estimation and Inference
2. Bayesian Analysis Options in R, and Coding for BUGS, JAGS, and Stan
3. Model Fit, Comparison, and Checking
4. Borrowing Strength via Hierarchical Estimation
5. Time Structured Priors
6. Representing Spatial Dependence
7. Regression Techniques Using Hierarchical Priors
8. Bayesian Multilevel Models
9. Factor Analysis, Structural Equation Models, and Multivariate Priors
10. Hierarchical Models for Longitudinal Data
11. Survival and Event History Models
12. Hierarchical Methods for Nonlinear and Quantile Regression
Biography
Peter Congdon is Research Professor in Quantitative Geography and Health Statistics at Queen Mary, University of London.
"...The material covered in the almost 600 pages is broad, rich, and presented in a dense and conciseway. There is a notable emphasis on longitudinal models, spatial applications as well as structural equations models, which seems natural given the focus on hierarchicalmodels...The readership that will benefit most from the book might be statisticians with intermediateor advanced-level expertise in Bayesian statistics and at least some basic experience in the software implementation of Bayesian modeling techniques. The second edition is particularly worthwhile since it catches up with the computational developments of the last decade. Overall, the book nicely illustrates the richness and the flexibility of hierarchical modeling options within the Bayesian framework."
- Christian Stock, Biometrical Journal, October 2020
