Title（问题）：Robust stepwise regression methods for high-dimensional variable selection

Speaker（报告人）：Prof.Jingshiang Hwang, Academia Sinica

Time（时间）：2012年6月7日（周四）下昼02:00-03:00

Place（所在）：伟易博新楼217课堂

Abstract（摘要）：Stepwise regression is a classical and very popular variable screening method which has been widely accepted by practical analysts. Wang (JASA, 2009) showed that forward regression with an extended Bayesian information criterion can identifytheoretically all relevant predictors consistently under an ultrahigh-dimensional setup withregular assumptions. Ing and Lai (Stat Sinica, 2011) further introduced a fast stepwise regression method which has oracle property under a strong sparsity assumption. Both methods showed very impressive performances in each own simulation scenarios respectively. However, each method performed unsatisfactorily under some of the other’s simulation scheme. It indicates that these screening methods of eleganttheoretical properties may be sensitive to their own assumptions. This study is motivated to develop a more robust stepwise method for screening high-dimensional datain practice. The idea is to establish a new stopping rule other than the conventional information criteria for lessening model assumptions.Numerical simulations studies have shown encouraging performance of the proposed methods in comparisons with several popular techniques in the literature including LASSO and ISIS proposed by Fan and Lv (JRSSB, 2008).