This article surveys Bayesian methods for categorical data analysis, with primary emphasis
on contingency table analysis. Early innovations were proposed by Good (1953, 1956 …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

Praise for the Second Edition" A must-have book for anyone expecting to do research and/or
applications in categorical data analysis."—Statistics in Medicine" It is a total delight reading …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Subjects often drop out of longitudinal studies prematurely, yielding unbalanced data with
unequal numbers of measures for each subject. Modern software programs for handling …

Complex multivariate testing problems are frequently encountered in many scientific
disciplines, such as engineering, medicine and the social sciences. As a result, modern …

Missing Data in Clinical Studies provides a comprehensive account of the problems arising
when data from clinical and related studies are incomplete, and presents the reader with …

Studies in the social and behavioral sciences frequently suffer from missing data. For
instance, sample surveys often have some individuals who either refuse to participate or do …

Due to recent theoretical findings and advances in statistical computing, there has been a
rapid development of techniques and applications in the area of missing data analysis …

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 …