Bayesian Models for Categorical Data
Wiley Series in Probability and Statistics
1. Edition May 2005
466 Pages, Hardcover
Practical Approach Book
Short Description
Categorical, or discrete, data is one of the most common types of data available. Bayesian methods are increasingly being used for the modeling of such data, yet there is no book available that provides an overview of Bayesian models for analyzing categorical data. Bayesian Models for Categorical Data provides such an overview, together with a huge number of worked examples to illustrate the methods. WinBUGS code for the examples will be made available via ftp, so that the reader can apply the methods to their own data. The book also includes exercises that require the student to do further analysis of the included examples, and enabling use of the book as a course text. The author has published two very successful books on Bayesian modeling, and this book complements those with additional material, particularly on the topic of missing data, and by focusing on modeling of categorical data.
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.
Chapter 2: Model Comparison and Choice.
Chapter 3: Regression for Metric Outcomes.
Chapter 4: Models for Binary and Count Outcomes.
Chapter 5: Further Questions in Binomial and Count Regression.
Chapter 6: Random Effect and Latent Variable Models for Multicategory Outcomes.
Chapter 7: Ordinal Regression.
Chapter 8: Discrete Spatial Data.
Chapter 9: Time Series Models for Discrete Variables.
Chapter 10: Hierarchical and Panel Data Models
Chapter 11: Missing-Data Models.
Index.
