Biostatistics Seminar: Jeff Wu

Presentation: Cmenet: A New Method for Bi-Level Variable Selection of Conditional Main Effects

Speaker: Jeff Wu, Ph.D., Coca-Cola Chair in Engineering Statistics and Professor, H. Milton Stewart School of Industrial & Systems Engineering, Georgia Tech

Abstract: This talk introduces a novel method for selecting main effects and a set of reparametrized predictors called conditional main effects (CMEs), which capture the conditional effect of a factor at a fixed level of another factor. CMEs represent interpretable, domain-specific phenomena for a wide range of applications in engineering, social sciences and genomics. The key challenge is in incorporating the grouped structure of CMEs within the variable selection procedure itself. We propose a new method, cmenet, which employs two principles called CME coupling and CME reduction to effectively navigate the selection algorithm. Simulation studies demonstrate the improved performance of cmenet over generic variable selection methods, such as Lasso and SparseNet. Applied to a gene association study on fly wing shape, cmenet not only provides improved predictive performance over existing selection techniques, but also reveals important insight on gene activation behavior, which could guide further experiments. (Joint work with Simon Mak.)

Date/Time
Thu, Nov 2, 2017, 3:30pm to 5:00pm
Location
Room T-639 (HST)
Sponsors

Faculty Coordinator: Noah Simon, nrsimon@uw.edu

Seminar Coordinator: Sandra Coke, sjcoke@uw.edu