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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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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SA.1 K49 (Browse shelf(Opens below)) Available C26265
Books ISI Library, Kolkata 000SA.1 K49 (Browse shelf(Opens below)) Available 134898
Total holds: 0

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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