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