03892cam a2200373 i 4500 2890 2890 17720877 OSt 20190927154407.0 130502s2014 flu b 101 0 eng 2013010114 9781439849637 (hardback : acidfree paper) DLC eng DLC rda DLC pcc QA276.8 .K55 2014 519.54 KIM S 23 MAT029000 bisacsh Kim, Jae Kwang, 1968- Statistical methods for handling incomplete data / Jae Kwang Kim, Department of Statistics Iowa State University, USA, Jun Shao, Department of Statistics University of Wisconsin - Madison, USA. Boca Raton : CRC Press, Taylor & Francis Group, [2014] xi, 211 pages ; 25 cm text rdacontent unmediated rdamedia volume rdacarrier 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 "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"-- Provided by publisher. Missing observations (Statistics) Multiple imputation (Statistics) MATHEMATICS / Probability & Statistics / General. bisacsh Shao, Jun (Statistician) 7 cbc orignew 1 ecip 20 y-gencatlg ddc STS 0 0 ddc 0 0 44/2 AIISH AIISH 2019-09-27 0 519.54 KIM S;1 19452 2019-09-27 00:00:00 2019-09-27 STS 0 0 ddc 0 0 R-21/2 AIISH AIISH 2020-03-07 0 519.54 KIM S 19451 2020-03-07 00:00:00 2020-03-07 RFT