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

By: Contributor(s): Material type: TextTextPublication details: Boca Raton : CRC Press, c2014.Description: xi, 211 p. : ill. ; 25 cmISBN:
  • 9781439849637 (hardback : acidfree paper)
Subject(s): DDC classification:
  • 000SA.1 23 K49
Contents:
1. Introduction-- 2. Likelihood-based approach -- 3. Computation -- 4. Imputation -- 5. Propensity scoring approach -- 6. Nonignorable missing data -- 7. Longitudinal and clustered data -- 8. Application to survey sampling -- 9. Statistical matching-- Bibliography-- Index.
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"--
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Includes bibliographical references (pages 201-207) and index.

1. Introduction--
2. Likelihood-based approach --
3. Computation --
4. Imputation --
5. Propensity scoring approach --
6. Nonignorable missing data --
7. Longitudinal and clustered data --
8. Application to survey sampling --
9. Statistical matching--

Bibliography--
Index.

"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"--

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