%0 Journal Article
%T Nonparametric Feature Screening via the Variance of the Regression Function
%A Won Chul Song
%A Michael G. Akritas
%J Open Journal of Statistics
%P 413-438
%@ 2161-7198
%D 2024
%I Scientific Research Publishing
%R 10.4236/ojs.2024.144017
%X This article develops a procedure for screening variables, in ultra high-di- mensional settings, based on their predictive significance. This is achieved by ranking the variables according to the variance of their respective marginal regression functions (RV-SIS). We show that, under some mild technical conditions, the RV-SIS possesses a sure screening property, which is defined by Fan and Lv (2008). Numerical comparisons suggest that RV-SIS has competitive performance compared to other screening procedures, and outperforms them in many different model settings.
%K Sure Independence Screening
%K Nonparametric Regression
%K Ultrahigh-Dimensional Data
%K Variable Selection
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=135483