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

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  • He, Shengmei
  • Ma, Shuangge
  • Xu, Wangli

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.

Suggested Citation

  • He, Shengmei & Ma, Shuangge & Xu, Wangli, 2019. "A modified mean-variance feature-screening procedure for ultrahigh-dimensional discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 155-169.
    • Handle: RePEc:eee:csdana:v:137:y:2019:i:c:p:155-169
    DOI: 10.1016/j.csda.2019.02.003
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    References listed on IDEAS

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    1. Hengjian Cui & Runze Li & Wei Zhong, 2015. "Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 630-641, June.
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    6. Fan, Jianqing & Feng, Yang & Song, Rui, 2011. "Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 544-557.
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