Correlation Pursuit: Stepwise Variable selection in Sufficient Dimension Reduction

Title（问题）：Correlation Pursuit: Stepwise Variable selection in Sufficient Dimension Reduction

Speaker（报告人）：Professor Michael Yu Zhu Purdue University

Time（时间）：2011年6月24日（周五）上午10:30-11:30

Place（所在）：北京大学理科一号楼（数学学院）1418课堂

Abstract（摘要）：In this talk, a stepwise procedure, correlation pursuit (COP), is proposed for variable selection under the sufficient dimension reduction framework. Unlike linear stepwise regression, COP does not impose assumptions on the exact form of the relationship between the response variable and the predictor variables. The COP procedure selects variables that attain the maximum correlation between the transformed response and the linear combination of the variables. Some asymptotic properties of the COP procedure are established, and in particular, its variable selection performance under diverging sample size and number of predictors has been investigated. The empirical performance of the COP procedure in comparison with other existing methods is demonstrated by both simulation studies and a real life example in functional genomics.

About the speaker（报告人先容）：

北京大学统计科学中心