B0979
Title: On the use of minimum penalties in statistical learning
Authors: Ben Sherwood - University of Kansas (United States) [presenting]
Abstract: Modern multivariate machine learning and statistical methodologies estimate parameters of interest while leveraging prior knowledge of the association between outcome variables. We propose the MinPen framework to simultaneously estimate regression coefficients associated with the multivariate regression model and the relationships between outcome variables using common assumptions. The MinPen framework utilizes a novel penalty based on the minimum function to detect and exploit relationships between responses simultaneously. An iterative algorithm is proposed as a solution to the non-convex optimization. Theoretical results such as high dimensional convergence rates, model selection consistency, and a framework for post-selection inference are provided. We extend the proposed MinPen framework to other exponential family loss functions, with a specific focus on multiple binomial responses.
