TY  - BOOK
AU  - Kim,Jae Kwang
AU  - Shao,Jun
TI  - Statistical methods for handling incomplete data
SN  - 9781439849637 (hardback : acidfree paper)
AV  - QA276.8 .K55 2014
U1  - 519.54 KIM S 23
PY  - 2014///]
CY  - Boca Raton
PB  - CRC Press, Taylor & Francis Group
KW  - Missing observations (Statistics)
KW  - Multiple imputation (Statistics)
KW  - MATHEMATICS / Probability & Statistics / General
KW  - bisacsh
N1  - Includes bibliographical references (pages 201-207) and index; 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
N2  - "With the advances in statistical computing, there has been a rapid development of techniques and applications in missing data analysis. This book aims to cover the most up-to-date statistical theories and computational methods for analyzing incomplete data through (1)vigorous treatment of statistical theories on likelihood-based inference with missing data, (2) comprehensive treatment of computational techniques and theories on imputation, and (3) most up-to-date treatment of methodologies involving propensity score weighting, nonignorable missing, longitudinal missing, survey sampling application, and statistical matching. The book is suitable for use as a textbook for a graduate course in statistics departments or as a reference book for those interested in this area. Some of the research ideas introduced in the book can be developed further for specific applications"--
ER  -