Statistical methods for handling incomplete data
By: Jae Kwang Kim, Department of Statistics Iowa State University, USA, Jun Shao, Department of Statistics University of Wisconsin - Madison, USA
Contributors: Shao, Jun (Statistician)
Publisher: CRC Press, Taylor & Francis Group
Published: 2014
Place of publication: Boca Raton
Language: eng
Number of pages: 211
ISBN-13: 9781439849637
Subjects: Missing observations (Statistics); Multiple imputation (Statistics); MATHEMATICS / Probability & Statistics / General
Description: "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"--
Notes: Includes bibliographical references (pages 201-207) and index.