LAUSR

Feature screening in ultrahigh-dimensional varying coefficient models is a crucial statistical problem in economics, genomics, etc. Current methods not only suffer from circumstances when the models involve multiple index variables or group predictor variables, but also cannot handle nonlinear varying coefficient models. To address these reallife scenarios efficiently, we develop a screening procedure for ultrahigh-dimensional varying coefficient models utilizing conditional distance covariance (CDC). Extensive simulation studies and two real economic data examples show the effectiveness and the flexibility of our proposed method.