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Abstract Details

Activity Number: 466
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #304083
Title: Feature Screening for Varying Coefficient Models
Author(s): Jingyuan Liu*+ and Runze Li and Rongling Wu
Companies: Penn State University and Penn State University and Penn State University
Address: 325 Thomas Bldg, University Park, PA, 16802, United States
Keywords: varying coefficinet models ; feature screening ; conditional correlation ; sure screening property ; ranking consistency

Genome-wide association studies (GWAS) are challenged by the ultrahigh dimensionality of the single-nucleotide polymorphisms (SNPs) which often play the role of predictors, as well as by the fact that the SNP effects may vary among subjects through certain covariate. To address these two issues, we develop a novel two-stage approach, where in the first stage, a new feature screening procedure is proposed to reduce dimensionality specifically for varying coefficient models based on the conditional correlation, which is shown to possess the sure screening property and ranking consistency property; and in the second stage, several regularization methods, such as LASSO, Adaptive LASSO, and SCAD penalized regression, are modified for varying coefficient models to select important variables.

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