A modified mean-variance feature-screening procedure for ultrahigh-dimensional discriminant analysis

Shengmei He, Shuangge Ma and Wangli Xu

Computational Statistics & Data Analysis, 2019, vol. 137, issue C, 155-169

Abstract: Cui et al. (2015) proposed a mean–variance feature-screening method based on the index MV(X|Y). By modifying MV(X|Y) with a weight function, a new index AD(X,Y) is introduced to measure the dependence between X and Y, and a corresponding feature-screening procedure called Anderson–Darling sure independence screening (AD-SIS) is proposed for ultrahigh-dimensional discriminant analysis. The sure screening and ranking consistency properties are established under mild conditions. It is shown that AD-SIS is model free with no specification of model structure and can be applied to multi-classification. Furthermore, AD-SIS is robust against heavy-tailed distributions. As such, it can be used to identify the tail difference for the covariate’s distribution. The finite-sample performance of AD-SIS is assessed by simulation and real data analysis. The results show that, compared with existing methods, AD-SIS can be more competitive for feature screening for ultrahigh-dimensional discriminant analysis.

Keywords: Ultrahigh-dimensional data; Feature screening; Sure screening; Model free (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:137:y:2019:i:c:p:155-169

DOI: 10.1016/j.csda.2019.02.003

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