Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis

Hengjian Cui Capital Normal University Runze Li Pennsylvania State University Wei Zhong Xiamen University

Statistics Theory and Methods mathscidoc:1903.33001

Journal of the American Statistical Association, 110, (510), 630-641, 2015.6
This work is concerned with marginal sure independence feature screening for ultrahigh dimensional discriminant analysis. The response variable is categorical in discriminant analysis. This enables us to use the conditional distribution function to construct a new index for feature screening. In this article, we propose a marginal feature screening procedure based on empirical conditional distribution function. We establish the sure screening and ranking consistency properties for the proposed procedure without assuming any moment condition on the predictors. The proposed procedure enjoys several appealing merits. First, it is model-free in that its implementation does not require specification of a regression model. Second, it is robust to heavy-tailed distributions of predictors and the presence of potential outliers. Third, it allows the categorical response having a diverging number of classes in the order of O(n^κ ) with some κ ≥ 0. We assess the finite sample property of the proposed procedure byMonte Carlo simulation studies and numerical comparison.We further illustrate the proposed methodology by empirical analyses of two real-life datasets. Supplementary materials for this article are available online.
Ultrahigh dimensional data analysis, feature screening, discriminant analysis
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  • ESI Top 1% highly cited paper
  title={Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis},
  author={Hengjian Cui, Runze Li, and Wei Zhong},
  booktitle={Journal of the American Statistical Association},
Hengjian Cui, Runze Li, and Wei Zhong. Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis. 2015. Vol. 110. In Journal of the American Statistical Association. pp.630-641.
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