A Robust Model-Free Feature Screening Method for Ultrahigh-Dimensional Data.

ABSTRACT: Feature screening plays an important role in dimension reduction for ultrahigh-dimensional data. In this paper, we introduce a new feature screening method and establish its sure independence screening property under the ultrahigh-dimensional setting. The proposed method works based on the nonparanormal transformation and Henze-Zirkler's test; that is, it first transforms the response variable and features to Gaussian random variables using the nonparanormal transformation and then tests the dependence between the response variable and features using the Henze-Zirkler's test. The proposed method enjoys at least two merits. First, it is model-free, which avoids the specification of a particular model structure. Second, it is condition-free, which does not require any extra conditions except

SUBMITTER: Xue J 

PROVIDER: S-EPMC6284821 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature