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 23
PY  - 2014///
CY  - Boca Raton, Florida : 
PB  - CRC press
KW  - Missing observations (Statistics)
KW  - Multiple imputation (Statistics)
KW  - MATHEMATICS / Probability & Statistics / General
N1  - Includes bibliographical references and index; IntroductionIntroduction Outline How to Use This BookLikelihood-Based ApproachIntroductionObserved LikelihoodMean Score ApproachObserved InformationComputation IntroductionFactoring Likelihood ApproachEM AlgorithmMonte Carlo Computation Monte Carlo EM Data AugmentationImputationIntroductionBasic Theory for ImputationVariance Estimation after Imputation Replication Variance EstimationMultiple ImputationFractional ImputationPropensity Scoring Approach Introduction Regression Weighting Method Propensity Score Method Optimal Estimation Doubly Robust Method Empirical Likelihood Method Nonparametric MethodNonignorable 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) ExperimentLongitudinal and Clustered DataIgnorable Missing Data Nonignorable Monotone Missing DataPast-Value-Dependent Missing DataRandom-Effect-Dependent Missing DataApplication 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

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  -