Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis.

ABSTRACT: This work is concerned with marginal sure independence feature screening for ultra-high dimensional discriminant analysis. The response variable is categorical in discriminant analysis. This enables us to use conditional distribution function to construct a new index for feature screening. In this paper, 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 prese

SUBMITTER: Cui H 

PROVIDER: S-EPMC4574103 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature