Statistical Analysis with Missing Data, 2nd EditionISBN: 9780471183860
408 pages
September 2002

"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 reallife examples, stimulating endofchapter exercises, and uptodate references. It should be on every applied statistician’s 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 missingdata 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 uptodate, reorganized survey of current methodology for handling missingdata 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 missingdata mechanism and apply the theory to a wide range of important missingdata 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 datagenerating and missingdata 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 News asked three review editors to rate their top five favorite books in the September 2003 issue. Statistical Analysis With Missing Data was among those chosen.
PART I: OVERVIEW AND BASIC APPROACHES.
Introduction.
Missing Data in Experiments.
CompleteCase and AvailableCase Analysis, Including Weighting Methods.
Single Imputation Methods.
Estimation of Imputation Uncertainty.
PART II: LIKELIHOODBASED APPROACHES TO THE ANALYSIS OF MISSING DATA.
Theory of Inference Based on the Likelihood Function.
Methods Based on Factoring the Likelihood, Ignoring the MissingData Mechanism.
Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse.
LargeSample Inference Based on Maximum Likelihood Estimates.
Bayes and Multiple Imputation.
PART III: LIKELIHOODBASED APPROACHES TO THE ANALYSIS OF MISSING DATA: APPLICATIONS TO SOME COMMON MODELS.
Multivariate Normal Examples, Ignoring the MissingData Mechanism.
Models for Robust Estimation.
Models for Partially Classified Contingency Tables, Ignoring the MissingData Mechanism.
Mixed Normal and Nonnormal Data with Missing Values, Ignoring the MissingData Mechanism.
Nonignorable MissingData Models.
References.
Author Index.
Subject Index.
DONALD B. RUBIN, PhD, is the Chair of the Department of Statistics at Harvard University.
 Book aims to survey current methodology for handling missingdata problems
 Presents a likelihoodbased theory for analysis with missing data that systematizes the methods and provides a basis for future advances
 Part I discusses historical appraches to missingvalue 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 datagenerating 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 selfcontained and does not require specialized knowledge
“…a well written and well documented text for missing data analysis...” (Statistical Methods in Medical Research, Vol.14, No.1, 2005)
"An update to this authoritative book is indeed welcome." (Journal of the American Statistical Association, December 2004)
“…this is an excellent book. It is well written and inspiring…” (Statistics in Medicine, 2004; 23)
"...this second edition offers a thoroughly uptodate, reorganized survey of of current methods for handling missing data problems..." (Zentralblatt Math, Vol.1011, No.11, 203)
"...well written and very readable...a comprehensive, update treatment of an important topic by two of the leading researchers in the field. In summary, I highly recommend this book..." (Technometrics, Vol. 45, No. 4, November 2003)
Statistical Analysis with Missing Data, 2nd Edition (US $168.00)
and Robust Regression and Outlier Detection (US $148.00)
Total List Price: US $316.00
Discounted Price: US $237.00 (Save: US $79.00)