Table of contents for Statistical analysis with missing data / Roderick J. Little, Donald B. Rubin.


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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.