Efficient Feature Screening for Ultrahigh-dimensional Varying Coefficient Models
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.
June 16th, 2017
09:00 ~ 10:00
Chen Xin, National University of Singapore
Room 308, School of Information Management & Engineering, Shanghai University of Finance & Economics