Statistical Analysis with Missing Data (Hardcover, 2)

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.