Provides timely information for a fast-developing area Presents the first extensive
treatment of Markov Chain Monte Carlo algorithms for multivariate missing-data problems
Offers clear, detailed presentation of the theory and practice of EM, data augmentation,
and multiple imputation
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 presents a unified,
Bayesian approach to the analysis of incomplete multivariate data, covering datasets in
which the variables are continuous, categorical, or both. The focus is applied, where
necessary, to help readers thoroughly understand the statistical properties of those
methods, and the behavior of the accompanying algorithms.
All techniques are illustrated with real data examples, with extended discussion and
practical advice. All of the algorithms described in this book have been implemented by
the author for general use in the statistical languages S and S Plus. The software is
available free of charge on the Internet.
Table of Contents
Introduction
Assumptions
EM and Inference by Data Augmentation
Methods for Normal Data
More on the Normal Model
Methods for Categorical Data
Loglinear Models
Methods for Mixed Data
Further Topics
Appendices
References
Index
Hardcover
430 pages
