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Abstract:
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This short course will discuss methods for the statistical analysis of data sets with missing values. Topics will include: Definition of missing data; assumptions about mechanisms, including missing at random; pros and cons of simple methods such as complete-case analysis, naïve imputation etc; Weighting methods; multiple imputation; maximum likelihood and Bayesian inference with missing data; computational techniques, included EM algorithm and extensions, and Gibbs sampler; software for handling missing data; missing data in common statistical applications, including regression, repeated-measures analysis, clinical trials. Selection and pattern-mixture models for nonrandom nonresponse.
Prerequisites: Course requires knowledge of standard statistical models such as the multivariate normal, multiple linear regression, contingency tables, as well as matrix algebra, calculus, and basic maximum likelihood for common distributions.
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