Tue, February 7, 2012
All Day
209 W Eighteenth Ave (EA), Room 170
Title
Variable Screening Methods for High-dimensional Feature Space
Speaker
Rui Song, Colorado State University
Abstract
High-dimension is an important feature of modern statistical applications, where the dimensionality of covariates is potentially much larger than sample size. Correlation screening (Fan and Lv, 2008) was established to be effective to reduce the dimension for such data while achieve the sure screening property for the linear models, that is, all the important variables can be retained with overwhelming probability. In this talk we will study several screening methods with high dimensional data. The sure screening property is established. Simulations and real data analysis demonstrate that proposed methods have very competitive performance.