A model-free feature screening approach based on kernel density estimation. Issue 12 (13th August 2017)

• Record Type: Journal Article
• Authors: Li, Xiangjie; Wang, Lei; Zhang, Jingxiao
• Is Part Of: Journal of statistical computation and simulation. Volume 87:Issue 12(2017)
• Page Start: 2450
• Page End: 2468
• Publication Date: 2017-08-13
• Subjects: Ultrahigh-dimensional -- feature screening -- probability density function distance -- kernel density estimate -- composite Simpson's rule; 62G07 -- 65D30 -- 62F07; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2017.1334779
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: In this article, a new model-free feature screening method named after probability density (mass) function distance (PDFD) correlation is presented for ultrahigh-dimensional data analysis. We improve the fused-Kolmogorov filter (F-KOL) screening procedure through probability density distribution. The proposed method is also fully nonparametric and can be applied to more general types of predictors and responses, including discrete and continuous random variables. Kernel density estimate method and numerical integration are applied to obtain the estimator we proposed. The results of simulation studies indicate that the fused-PDFD performs better than other existing screening methods, such as F-KOL filter, sure-independent screening (SIS), sure independent ranking and screening (SIRS), distance correlation sure-independent screening (DCSIS) and robust ranking correlation screening (RRCS). Finally, we demonstrate the validity of fused-PDFD by a real data example.