Description
Taylor & Francis Ltd Statistical Methods For Handling Incomplete Data 2013 Edition by Jae Kwang Kim, Jun Shao
Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.Suitable for graduate students and researchers in statistics, the book presents thorough treatments of: Statistical theories of likelihood-based inference with missing dataComputational techniques and theories on imputationMethods involving propensity score weighting, nonignorable missing data, longitudinal missing data, survey sampling, and statistical matchingAssuming prior experience with statistical theory and linear models, the text uses the frequentist framework with less emphasis on Bayesian methods and nonparametric methods. It includes many examples to help readers understand the methodologies. Some of the research ideas introduced can be developed further for specific applications. 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