Statistical methods for handling incomplete data
"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 inv…
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- Full title :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.
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"--
Read More ↓Edition Statements
- Publication Date:2014
Publication Statements
- Publisher(s):CRC Press, Taylor & Francis Group
- Place of Publication:Boca Raton
- Language:eng
- Publication Date:2014
The Physical Object
- Description:xi, 211 pages ; : 25 cm.
Edition Identifiers
- ISBN:9781439849637, 1439849633
- oclc:ocn613423472
- LC Classification:QA276.8
- Dewey Decimal:519.5/4
Table of Contents
Likelihood-based approach -- Computation -- Imputation -- Propensity scoring approach --Nonignorable missing data -- Longitudinal and clustered data -- Application to survey sampling -- Statistical matching.
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