Written by a team of leading researchers in the field, this handbook presents a comprehensive overview of the state of the art in the theory and applications of missing data analysis. It covers historical developments, Bayesian and likelihood methods, semiparametric methods, multiple imputation, sensitivity analysis, and other special topics. Accessible to graduate students and researchers, each chapter provides sufficient background material, worked examples, case studies, and software, where applicable.
Preliminaries. Likelihood and Bayesian Methods, Semiparametric Methods. Multiple Imputation. Sensitivity Analysis.
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