Penalized Regression Methods for Ranking Variables by Effect Size, with Applications to Genetic Mapping Studies

• Activity Number: 599
• Type: Invited
• Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Nam-Hee Choi and Kerby Shedden and Ji Zhu*+
• Companies: University of Michigan and University of Michigan and University of Michigan
• Keywords: Variable Selection ; Ridge Regression ; Lasso ; Genetic Mapping

Abstract

Multiple regression can be used to rank predictor variables according to their "unique" association with a response variable - that is, the association that is not explained by other measured predictors. Such a ranking is useful in applications such as genetic mapping studies, where one goal is to clarify the relative importance of several correlated genetic variants with weak effects. The use of classical multiple regression to rank the predictors according to their unique associations with the response is limited by difficulties due to collinearities among the predictors. Here we show that regularized regression can improve the accuracy of this ranking, with the greatest improvement occurring when the pairwise correlations among the predictor variables are strong and heterogeneous.