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Activity Number: 677 - Variable Selection Methods in Statistical Learning
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #329144
Title: Cmenet: a New Method for Bi-Level Variable Selection of Conditional Main Effects
Author(s): Simon Mak* and C. F. Jeff Wu
Companies: Georgia Institute of Technology and Georgia Institute of Technology
Keywords: conditional effects; coordinate descent; gene association; interaction analysis; variable selection

This talk introduces a novel method for selecting main effects and a set of reparametrized effects 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 implicit grouped structure of CMEs within the variable selection procedure itself. We propose a new method, called cmenet, which employs two principles called CME coupling and CME reduction to effectively navigate the selection algorithm. Simulation studies demonstrate the improved CME selection performance of cmenet over more generic selection methods. Applied to a gene association study on fly wing shape, cmenet not only yields more parsimonious models and improved predictive performance over standard two-factor interaction analysis methods, but also reveals important insights on gene activation behavior, which can be used to guide further experiments.

Authors who are presenting talks have a * after their name.

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