Bayesian Models for Categorical Data

Front Cover
John Wiley & Sons, Dec 13, 2005 - Mathematics - 446 pages
0 Reviews
The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes.
* Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data).
* Considers missing data models techniques and non-standard models (ZIP and negative binomial).
* Evaluates time series and spatio-temporal models for discrete data.
* Features discussion of univariate and multivariate techniques.
* Provides a set of downloadable worked examples with documented WinBUGS code, available from an ftp site.
The author's previous 2 bestselling titles provided a comprehensive introduction to the theory and application of Bayesian models. Bayesian Models for Categorical Data continues to build upon this foundation by developing their application to categorical, or discrete data - one of the most common types of data available. The author's clear and logical approach makes the book accessible to a wide range of students and practitioners, including those dealing with categorical data in medicine, sociology, psychology and epidemiology.
 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Chapter 1 Principles of Bayesian Inference
1
Chapter 2 Model Comparison and Choice
29
Chapter 3 Regression for Metric Outcomes
55
Chapter 4 Models for Binary and Count Outcomes
113
Chapter 5 Further Questions in Binomial and Count Regression
155
Chapter 6 Random Effect and Latent Variable Models for Multicategory Outcomes
197
Chapter 7 Ordinal Regression
235
Chapter 8 Discrete Spatial Data
267
Chapter 9 Time Series Models for Discrete Variables
289
Chapter 10 Hierarchical and Panel Data Models
321
Chapter 11 MissingData Models
379
Index
415
WILEY SERIES IN PROBABILITY AND STATISTICS
427
Copyright

Other editions - View all

Common terms and phrases

About the author (2005)

Peter Congdon, Queen Mary, University of London, UK
Peter is the author of two best-selling Wiley books on Bayesian modelling – Bayesian Statistical Modelling, and Applied Bayesian Modelling.

Bibliographic information