PHS 7090 - Advanced Statistical Inference I - Theory and Application

This is the first semester of a two-semester sequence. The sequence is designed for students who have had a Master’s level course in mathematical statistics. It prepares them to handle statistical inference in a wide range of problems at an advanced level -- thus strengthening their skillset for collaborating and for developing novel statistical methods. Topics covered include estimation and hypothesis testing via likelihoods, Bayesian inference, estimation, and hypothesis testing under misspecified likelihoods and semiparametric models, jackknife, bootstrap, rank, and permutation tests. Large sample theory will be discussed. Students will utilize the theory to analyze datasets and to compare the statistical properties of different inferential approaches. Students are expected to know R (statistical computing software).