Efficient Feature Screening for Ultrahigh-dimensional Varying Coefficient Models

Abstract

Feature screening in ultrahigh-dimensional varying coefficient models is a crucial statistical problem in economics, genomics and etc. Existing methods suffer in the cases of multiple index variables and group predictor variables. Moreover, current methods can not handle nonlinear varying coefficient models which is possible in reality.

To deal with those scenarios efficiently in real life, we develop a screening procedure for ultrahigh-dimensional varying coefficient models utilizing conditional distance covariance (CDC). Extensive stimulation studies and two real economic data examples have shown the effectiveness and the flexibility of our proposed methods.  

Time

June 16th, 2017

09:00 ~ 10:00

Speaker

Chen Xin, National University of Singapore

Room

Room 308, School of Information Management & Engineering, Shanghai University of Finance & Economics