Statistical Analysis with Missing Data, 2nd Edition
"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
—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 statistician’s bookshelf."
"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 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.
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.
DONALD B. RUBIN, PhD, is the Chair of the Department of Statistics at Harvard University.
- 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
“…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 up-to-date, 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)