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Fan, J.Q., Samworth, R. and Wu, Y.C. (2009) Ultrahigh Dimensional Feature Selection: BEYOND the linear Model. The Journal of Machine Learning Research, 10, 2013-2038.

has been cited by the following article:

• 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.