000			03892cam a2200373 i 4500
999			_c2890 _d2890
001			17720877
003			OSt
005			20190927154407.0
008			130502s2014 flu b 101 0 eng
010			_a 2013010114
020			_a9781439849637 (hardback : acidfree paper)
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
050	0	0	_aQA276.8 _b.K55 2014
082	0	0	_a519.54 KIM S _223
084			_aMAT029000 _2bisacsh
100	1		_aKim, Jae Kwang, _d1968-
245	1	0	_aStatistical methods for handling incomplete data / _cJae Kwang Kim, Department of Statistics Iowa State University, USA, Jun Shao, Department of Statistics University of Wisconsin - Madison, USA.
264		1	_aBoca Raton : _bCRC Press, Taylor & Francis Group, _c[2014]
300			_axi, 211 pages ; _c25 cm
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
504			_aIncludes bibliographical references (pages 201-207) and index.
505			_aIntroduction 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
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"-- _cProvided by publisher.
650		0	_aMissing observations (Statistics)
650		0	_aMultiple imputation (Statistics)
650		7	_aMATHEMATICS / Probability & Statistics / General. _2bisacsh
700	1		_aShao, Jun _c(Statistician)
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cSTS