Online Program

Return to main conference page
Saturday, June 1
Computational Statistics
Computational Statistics E-Posters
Sat, Jun 1, 9:30 AM - 10:30 AM
Grand Ballroom Foyer

Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems (306348)


Francesca Chiaromonte, Penn State University 
Runze Li, Penn State University 
*Debmalya Nandy, Penn State University 

Keywords: Ultrahigh dimension, Supervised problems, Sure Independence Screening, Model-free, Fisher information, Affymetrix GeneChip Rat Genome 230 2.0 Array.

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. Following the development 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 - Sure Independence Screening (CIS). CIS uses a marginal utility built upon Fisher Information, possesses the sure screening property, and is applicable to any type of response. Simulations and an application to transcriptomic data on rats reveal CIS' comparative strengths over some popular feature screening methods.