Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems.

ABSTRACT: Contemporary high-throughput experimental and surveying techniques give rise to ultrahigh-dimensional supervised problems with sparse signals; that is, a limited number of observations (n), each with a very large number of covariates (p >> n), only a small share of which is truly associated with the response. In these settings, major concerns on computational burden, algorithmic stability, and statistical accuracy call for substantially reducing the feature space by eliminating redundant covariates before the use of any sophisticated statistical analysis. Along the lines of Sure Independence Screening (Fan and Lv, 2008) and other model- and correlation-based feature screening methods, we propose a model-free procedure called Covariate Information Number - Sur

SUBMITTER: Nandy D 

PROVIDER: S-EPMC9512254 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature