Covariate Information for Sure Independence Feature Screening in Ultrahigh- Dimensional Supervised Problems

• Activity Number: 669 - New Nonparametric Statistical Methods for High-Dimensional Data
• Type: Contributed
• Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Debmalya Nandy* and Francesca Chiaromonte and Runze Li
• Companies: The Pennsylvania State University and The Pennsylvania State University and The Pennsylvania State University
• Keywords: Ultrahigh-dimension; Supervised problems; Sure Independence Screening; Model-free screening; Fisher Information; Affymetric GeneChip Rat Genome 230 2.0 Array Data

Abstract

Modern technological advances in various scientific fields generate ultrahigh-dimensional supervised data with sparse signals, i.e. a limited number of samples (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 application of any sophisticated statistical analysis. Following the development of Sure Independence Screening (Fan and Lv, 2008, JRSS-B) and other model- and correlation-based feature screening methods, we propose a model-free procedure called the Covariate Information Screening (CIS). CIS uses a marginal utility built upon Fisher Information, possesses the sure screening property, and is applicable to any type of response. An extensive simulation study and an application to transcriptomic data in rats reveal CIS's comparative strengths over some popular feature screening methods.