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
出版項
Boca Raton : CRC Press, Taylor & Francis Group, [2014]
Includes bibliographical references (pages 201-207) and index
Likelihood-based approach -- Computation -- Imputation -- Propensity scoring approach --Nonignorable missing data -- Longitudinal and clustered data -- Application to survey sampling -- Statistical matching
"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
