* Emphasizes the latest trends in the field. * Includes a new chapter on evolving methods. * Provides updated or revised material in most of the chapters.
About the Author
RODERICK J. A. LITTLE, PhD, is Professor and Chair of Biostatistics at the University of Michigan.
DONALD B. RUBIN, PhD, is the Chair of the Department of Statistics at Harvard University.
Features
Book aims to survey current methodology for handling missing-data problems
Presents a likelihood-based theory for analysis with missing data that systematizes the methods and provides a basis for future advances
Part I discusses historical appraches to missing-value problems
Part II presents a systematic apprach to the analysis of data with missing valuees, where inferences are based on likelihoods derived from formal statistical models for the data-generating and missing data mechanisms
Part III presents applications of hte approach in a variety of contexts including regressoin; factor analysis; contingency table analysis; time series; and sample survey inference
Briefly reviews basic principles of inferences based on likelihoods, expecting readers to be familiar with these concepts
Some chapters assume familiarity with analysis of variance for experimental designs; survey sampling; loglinear models for contingency tables
Specific examples introduce factor analysis, time series, etc.
Discussion of examples is self-contained and does not require specialized knowledge
