Special Topics II

1 Credit · Level: Graduate

Statistical analysis of data sets with missing values. Pros and cons of standard methods such as complete-case analysis, imputation. Likelihood-based inference for common statistical problems when data are missing, including regression, repeated-measures analysis, contingency table analysis. Selection and pattern-mixture models for nonrandom nonresponse. We will review computational tools include the EM algorithm and extensions, the Gibbs' sampler and multiple imputation.