000			03293cam a22003378i 4500
999			_c7833 _d7833
001			17720877
005			20210907120642.0
008			130502s2014 flu b 101 0 eng
010			_a 2013010114
020			_a9781439849637 (hardback : acidfree paper)
040			_aDLC _cDLC _erda
050	0	0	_aQA276.8 _b.K55 2014
082	0	0	_a519.54 _223 _bK.J.S
100	1		_aKim, Jae Kwang, _d1968- _eauthor
245	1	0	_aStatistical methods for handling incomplete data / _cJae Kwang Kim and Jun Shao.
264		1	_aBoca Raton, Florida : _bCRC press, _cc2014.
264		4	_cc2014.
300			_axi, 211pages ; _c22 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
504			_aIncludes bibliographical references and index.
505	0		_aIntroductionIntroduction 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
520			_a"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"--
650		0	_aMissing observations (Statistics)
650		0	_aMultiple imputation (Statistics)
650		7	_aMATHEMATICS / Probability & Statistics / General.
700	1		_aShao, Jun _eauthor
942			_cBK _2ddc