IntroductionIntroduction Outline How to Use This Book Likelihood-Based ApproachIntroductionObserved LikelihoodMean Score ApproachObserved Information Computation IntroductionFactoring Likelihood ApproachEM AlgorithmMonte Carlo Computation Monte Carlo EM Data Augmentation ImputationIntroductionBasic Theory for ImputationVariance Estimation after Imputation Replication Variance EstimationMultiple ImputationFractional Imputation Propensity Scoring Approach Introduction Regression Weighting Method Propensity Score Method Optimal Estimation Doubly Robust Method Empirical Likelihood Method Nonparametric Method Nonignorable Missing DataNonresponse Instrument Conditional Likelihood Approach Generalized Method of Moments (GMM) Approach Pseudo Likelihood Approach Exponential Tilting (ET) Model Latent Variable Approach Callbacks Capture?Recapture (CR) Experiment Longitudinal and Clustered DataIgnorable Missing Data Nonignorable Monotone Missing DataPast-Value-Dependent Missing DataRandom-Effect-Dependent Missing Data Application to Survey Sampling Introduction Calibration Estimation Propensity Score Weighting Method Fractional Imputation Fractional Hot Deck Imputation Imputation for Two-Phase Sampling Synthetic Imputation Statistical Matching Introduction Instrumental Variable Approach Measurement Error ModelsCausal Inference Bibliography Index