Bibliographic record and links to related information available from the Library of Congress catalog
Information from electronic data provided by the publisher. May be incomplete or contain other coding.
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.