Model-free feature screening for categorical outcomes: Nonlinear effect detection and false discovery rate control.

ABSTRACT: Feature screening has become a real prerequisite for the analysis of high-dimensional genomic data, as it is effective in reducing dimensionality and removing redundant features. However, existing methods for feature screening have been mostly relying on the assumptions of linear effects and independence (or weak dependence) between features, which might be inappropriate in real practice. In this paper, we consider the problem of selecting continuous features for a categorical outcome from high-dimensional data. We propose a powerful statistical procedure that consists of two steps, a nonparametric significance test based on edge count and a multiple testing procedure with dependence adjustment for false discovery rate control. The new method presents two novelties. First, the edge-count t

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC6544247 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature