This book presents a unified, Bayesian approach to the analysis of inc omplete multivariate data, covering datasets in which the variables ar e continuous, categorical, or both. It is written for applied statisti cians, biostatisticians, practitioners of sample surveys, graduate stu dents, and other methodologically-orientated researchers in search of practical tools to handle missing data. The focus is applied where ne cessary to help readers thoroughly understand the statistical properti es of those methods, and the behavior of the accompanying algorithms. All techniques are illustrated...

The last two decades have seen enormous developments in statistical methods for incomplete data. The EM algorithm and its extensions, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data problems. Yet, in practical terms, those developments have had surprisingly little impact on the way most data analysts handle missing values on a routine basis. Analysis of Incomplete Multivariate Data helps bridge the gap between theory and practice, making these missing-data tools accessible to a broad audience. It...