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Abstract Details
Activity Number:
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345
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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Abstract - #304901 |
Title:
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Structured Dimension Reduction
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Author(s):
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Bo Zhang*+ and Lexin Li
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Companies:
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North Carolina State University and North Carolina State University
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Address:
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Department of Statistics, Raleigh, NC, 27695-8203, United States
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Keywords:
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Dimension reduction ;
conditional independence ;
reproducing kernel
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Abstract:
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We consider high-dimensional regression problem where the predictors have some structural constraint. For instance, in cognitive science, a battery of cognitive measures belong to different cognitive domains; in computational biology, genes belong to different biological pathways; and in brain imaging analysis, images come in the form of matrix/arrays. We propose a new class of dimension reduction approaches for aggregating high-dimensional predictors while incorporating predictor structural information. Our solution is based upon a general nonparametric characterization of conditional independence using covariance operators on reproducing kernel Hilbert spaces. Through simulation and real data analyses, we show that the proposed methods achieve greater accuracy and interpretability than the dimension reduction solutions that ignore predictor structural information.
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