Jae Kwang Kim, Jun Shao

Statistical Methods for Handling Incomplete Data

• Gebundenes Buch

Produktbeschreibung

Along with many examples, this text covers the most up-to-date statistical theories and computational methods for analyzing incomplete data. It presents a thorough treatment of statistical theories of likelihood-based inference with missing data. It also discusses numerous computational techniques and theories on imputation and extensively covers methods involving propensity score weighting, nonignorable missing data, longitudinal missing data, survey sampling, and statistical matching. Some of the research ideas introduced can be developed further for specific applications.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 223
• Erscheinungstermin: 23. Juli 2013
• Englisch
• Abmessung: 241mm x 161mm x 23mm
• Gewicht: 521g
• ISBN-13: 9781439849637
• ISBN-10: 1439849633
• Artikelnr.: 36964124

Inhaltsangabe

1. Introduction
2. Likelihood-based Approach
3. Computation
4. Imputation
5. Multiple Imputation
6. Fractional Imputation
7. Propensity Scoring Approach
8. Nonignorable Missing Data
9. Longitudinal and Clustered Data
10. Application to Survey Sampling
11. Data Integration
12. Advanced Topics

Rezensionen

"As a general comment, I must say that it is probably one of the most extensive, detailed and complete sources of information on the most up-to-date methods to deal with missing data, from simple imputation methods to more complex analysis techniques that take missingness into account. The book is well organized in 12 chapters that although could be read independently based on the readers needs/interest, it does have a hierarchy that makes sense going from more simple early chapters to more complex subjects later in the book."
~David Manteigas, ISCB Book Reviews