Statistical Methods for Handling Incomplete Data

Jae Kwang Kim, Jun Shao

July 23, 2013 by Chapman and Hall/CRC
Textbook - 223 Pages - 2 B/W Illustrations
ISBN 9781439849637 - CAT# K12249


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  • Uses the mean score equation as a building block for developing the theory for missing data analysis
  • Provides comprehensive coverage of computational techniques for missing data analysis
  • Presents a rigorous treatment of imputation techniques, particularly fractional imputation
  • Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data
  • Describes a survey sampling application


Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.

Suitable for graduate students and researchers in statistics, the book presents thorough treatments of:

  • Statistical theories of likelihood-based inference with missing data
  • Computational techniques and theories on imputation
  • Methods involving propensity score weighting, nonignorable missing data, longitudinal missing data, survey sampling, and statistical matching

Assuming prior experience with statistical theory and linear models, the text uses the frequentist framework with less emphasis on Bayesian methods and nonparametric methods. It includes many examples to help readers understand the methodologies. Some of the research ideas introduced can be developed further for specific applications.