Author(s)

Won Chul Song¹, Michael G. Akritas²

¹Department of Mathematics, Milwaukee School of Engineering, Milwaukee, USA.
²Department of Statistics, Pennsylvania State University, University Park, USA.

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.

Sure Independence Screening, Nonparametric Regression, Ultrahigh-Dimensional Data, Variable Selection

Song, W. and Akritas, M. (2024) Nonparametric Feature Screening via the Variance of the Regression Function. Open Journal of Statistics, 14, 413-438. doi: 10.4236/ojs.2024.144017.