Synopses & Reviews
Praise for the First Edition of Statistical Analysis with Missing Data
"An important contribution to the applied statistics literature.... I give the book high marks for unifying and making accessible much of the past and current work in this important area."
—William E. Strawderman, Rutgers University
"This book...provide[s] interesting real-life examples, stimulating end-of-chapter exercises, and up-to-date references. It should be on every applied statisticians bookshelf."
—The Statistician
"The book should be studied in the statistical methods department in every statistical agency."
—Journal of Official Statistics
Statistical analysis of data sets with missing values is a pervasive problem for which standard methods are of limited value. The first edition of Statistical Analysis with Missing Data has been a standard reference on missing-data methods. Now, reflecting extensive developments in Bayesian methods for simulating posterior distributions, this Second Edition by two acknowledged experts on the subject offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing-data problems.
Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing-data mechanism and apply the theory to a wide range of important missing-data problems.
The new edition now enlarges its coverage to include:
- Expanded coverage of Bayesian methodology, both theoretical and computational, and of multiple imputation
- Analysis of data with missing values where inferences are based on likelihoods derived from formal statistical models for the data-generating and missing-data mechanisms
- Applications of the approach in a variety of contexts including regression, factor analysis, contingency table analysis, time series, and sample survey inference
- Extensive references, examples, and exercises
Amstat Newsasked three review editors to rate their top five favorite books in the September 2003 issue. Statistical Analysis With Missing Datawas among those chosen.
Synopsis
Acknowledged experts on the subject bring together diverse sources on methods for statistical analysis of data sets with missing values, a pervasive problem for which standard methods are of limited value. Blending theory and application, it reviews historical approaches to the subject, and rigorous yet simple methods for multivariate analysis with missing values. Goes on to provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism. The theory is applied to a wide range of important missing-data problems. Extensive references, examples, and exercises.
Synopsis
* 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.
Table of Contents
Preface.
PART I: OVERVIEW AND BASIC APPROACHES.
Introduction.
Missing Data in Experiments.
Complete-Case and Available-Case Analysis, Including Weighting Methods.
Single Imputation Methods.
Estimation of Imputation Uncertainty.
PART II: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA.
Theory of Inference Based on the Likelihood Function.
Methods Based on Factoring the Likelihood, Ignoring the Missing-Data Mechanism.
Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse.
Large-Sample Inference Based on Maximum Likelihood Estimates.
Bayes and Multiple Imputation.
PART III: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA: APPLICATIONS TO SOME COMMON MODELS.
Multivariate Normal Examples, Ignoring the Missing-Data Mechanism.
Models for Robust Estimation.
Models for Partially Classified Contingency Tables, Ignoring the Missing-Data Mechanism.
Mixed Normal and Nonnormal Data with Missing Values, Ignoring the Missing-Data Mechanism.
Nonignorable Missing-Data Models.
References.
Author Index.
Subject Index.