TITLE:
Nonparametric Feature Screening via the Variance of the Regression Function
AUTHORS:
Won Chul Song, Michael G. Akritas
KEYWORDS:
Sure Independence Screening, Nonparametric Regression, Ultrahigh-Dimensional Data, Variable Selection
JOURNAL NAME:
Open Journal of Statistics,
Vol.14 No.4,
August
26,
2024
ABSTRACT: 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.