Model selection is ubiquitous in modern statistical applications. When the model is chosen after viewing the data, classical procedures for statistical inference are no longer valid. The study of post-selection inference in the context of inference for linear regression coefficients after variable selection is one of the most popular and important topics in statistics today. In this course, we will explain the sources of the problem, discuss the different perspectives on what are the inferential targets and goals, and present cutting-edge solutions to the problem of post-selection inference. Paradigms to be studied include high-dimensional or post-regularization inference, simultaneous inference intended to control familywise error rates, and selective inference to control false discovery rates for selected parameters. The material will be taught at the level of advanced undergraduates, and is also suitable for graduate students having the necessary background. Prerequisites: Math 493, Math 494, Math 439 and experience using R.
Course Attributes: FA NSMAR NSMAS NSM
Section 01Topics in Statistics
INSTRUCTOR: KuffnerView Course Listing