Abstract Details
Activity Number:
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80
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Type:
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Contributed
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Date/Time:
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Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract #312600
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Title:
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Principle Components Adjusted Variable Screening Method for Ultrahigh Dimensional Feature Space
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Author(s):
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Zhongkai Liu*+ and Rui Song and Donglin Zeng
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Companies:
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North Carolina State University and North Carolina State University and University of North Carolina at Chapel Hill
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Keywords:
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Principle component ;
Variable selection ;
Sure screening
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Abstract:
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The problem of ultrahigh dimensional regression, i.e. the dimension of variables p is much larger than the sample size n, is quite common in various areas of modern scientific research. Based on the assumption that usually only a relatively small subset of the predictors contribute to the response, researchers have innovated many variable selection methods so far, including penalized pseudo-likelihood methods, and sure independence screening method based on the correlation learning of the marginal model. However, realizing the disadvantages of marginal models, methods based on conditional models came into being. In this paper, we propose a principle components adjusted variable screening method, which uses top principle components as surrogate covariates to account for the variability of the omitted predictors. The efficiency of the method is illustrated by simulation studies and analysis of real data sets.
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Authors who are presenting talks have a * after their name.
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