Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.
Suitable for graduate students and researchers in statistics, the book presents thorough treatments of:
- Statistical theories of likelihood-based inference with missing data
- Computational techniques and theories on imputation
- Methods involving propensity score weighting, nonignorable missing data, longitudinal missing data, survey sampling, and statistical matching
Assuming prior experience with statistical theory and linear models, the text uses the frequentist framework with less emphasis on Bayesian methods and nonparametric methods. It includes many examples to help readers understand the methodologies. Some of the research ideas introduced can be developed further for specific applications.
Table of Contents:
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
