Statistical Methods for Handling Incomplete Data

Introduction
Introduction
Outline
How to Use This Book

Likelihood-Based Approach
Introduction
Observed Likelihood
Mean Score Approach
Observed Information

Computation
Introduction
Factoring Likelihood Approach
EM Algorithm
Monte Carlo Computation
Monte Carlo EM
Data Augmentation

Imputation
Introduction
Basic Theory for Imputation
Variance Estimation after Imputation
Replication Variance Estimation
Multiple Imputation
Fractional Imputation

Propensity Scoring Approach
Introduction
Regression Weighting Method
Propensity Score Method
Optimal Estimation
Doubly Robust Method
Empirical Likelihood Method
Nonparametric Method

Nonignorable Missing Data
Nonresponse 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 Data
Ignorable Missing Data
Nonignorable Monotone Missing Data
Past-Value-Dependent Missing Data
Random-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 Models
Causal Inference

Bibliography

Index