Peter D. Congdon

Bayesian Hierarchical Models (eBook, ePUB)

With Applications Using R, Second Edition

• Format: ePub

Produktbeschreibung

This is the second edition of a book on applied Bayesian modelling using WinBUGS. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 592
• Erscheinungstermin: 16. September 2019
• Englisch
• ISBN-13: 9780429532900
• Artikelnr.: 57730508

Autorenporträt

Peter Congdon is Research Professor in Quantitative Geography and Health Statistics at Queen Mary, University of London.

Inhaltsangabe

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

Rezensionen

"...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