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.