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Cover Art
Author Kim, Jae Kwang, 1968- author.

Title Statistical methods for handling incomplete data / Jae Kwang Kim, Jun Shao.

Published Boca Raton, FL : CRC Press, [2014]


Location Call No. Status
 UniM ERC  519.54 KIM    AVAILABLE
Physical description xi, 211 pages : illustrations ; 25 cm.
Series A Chapman & Hall book.
Chapman & Hall book.
Bibliography Includes bibliographical references and index.
Contents 1.Introduction -- 1.1.Introduction -- 1.2.Outline -- 1.3.How to use this book -- 2.Likelihood-based approach -- 2.1.Introduction -- 2.2.Observed likelihood -- 2.3.Mean score approach -- 2.4.Observed information -- 3.Computation -- 3.1.Introduction -- 3.2.Factoring likelihood approach -- 3.3.EM algorithm -- 3.4.Monte Carlo computation -- 3.5.Monte Carlo EM -- 3.6.Data augmentation -- 4.Imputation -- 4.1.Introduction -- 4.2.Basic theory for imputation -- 4.3.Variance estimation after imputation -- 4.4.Replication variance estimation -- 4.5.Multiple imputation -- 4.6.Fractional imputation -- 5.Propensity scoring approach -- 5.1.Introduction -- 5.2.Regression weighting method -- 5.3.Propensity score method -- 5.4.Optimal estimation -- 5.5.Doubly robust method -- 5.6.Empirical likelihood method -- 5.7.Nonparametric method -- 6.Nonignorable missing data -- 6.1.Nonresponse instrument -- 6.2.Conditional likelihood approach --
Contents note continued: 6.3.Generalized method of moments (GMM) approach -- 6.4.Pseudo likelihood approach -- 6.5.Exponential tilting (ET) model -- 6.6.Latent variable approach -- 6.7.Callbacks -- 6.8.Capture-recapture (CR) experiment -- 7.Longitudinal and clustered data -- 7.1.Ignorable missing data -- 7.2.Nonignorable monotone missing data -- 7.2.1.Parametric models -- 7.2.2.Nonparametric p(y/x) -- 7.2.3.Nonparametric propensity -- 7.3.Past-value-dependent missing data -- 7.3.1.Three different approaches -- 7.3.2.Imputation models under past-value-dependent nonmonotone missing -- 7.3.3.Nonparametric regression imputation -- 7.3.4.Dimension reduction -- 7.3.5.Simulation study -- 7.3.6.Wisconsin Diabetes Registry Study -- 7.4.Random-effect-dependent missing data -- 7.4.1.Three existing approaches -- 7.4.2.Summary statistics -- 7.4.3.Simulation study -- 7.4.4.Modification of diet in renal disease -- 8.Application to survey sampling -- 8.1.Introduction --
Contents note continued: 8.2.Calibration estimation -- 8.3.Propensity score weighting method -- 8.4.Fractional imputation -- 8.5.Fractional hot deck imputation -- 8.6.Imputation for two-phase sampling -- 8.7.Synthetic imputation -- 9.Statistical matching -- 9.1.Introduction -- 9.2.Instrumental variable approach -- 9.3.Measurement error models -- 9.4.Causal inference.
Summary "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"--
Other author Shao, Jun (Statistician)
Subject Missing observations (Statistics)
Multiple imputation (Statistics)
ISBN 9781439849637 (hardback : acid-free paper)
1439849633 (hardback : acid-free paper)