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.
“…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)
- 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