Statistical Analysis with Missing Data

3 credits

Missing data is a major issue in statistical analysis. This course introduces the four common approaches for inference in models with missing values, including maximum likelihood, multiple imputation, fully Bayesian, and weighted estimating equations. Computational tools (e.g. the EM algorithm and the Gibbs' sampler) will be discussed.