Abstract Details
Activity Number:
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349
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #310240 |
Title:
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Fast and Robust Association Testing for High-Throughput Testing
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Author(s):
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Fred Wright*+ and Yihui Zhou
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Companies:
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The University of North Carolina and University of North Carolina, Chapel Hill
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Keywords:
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permutation ;
high dimensional testing ;
moment matching
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Abstract:
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Permutation is an attractive approach to assess association between two vectors x and y, by comparing the observed statistic to the distribution induced by random permutation of one of the vectors. For a number of "standard" statistics, equivalent testing can be performed by using the sample Pearson correlation. Applications include the standard tests applied in the two-sample problem, simple linear regression, several generalized linear models, linear categorical trend tests, and rank-based association. We describe a simple approximation to the distribution of the correlation under permutation, providing accurate p-values that can be quickly computed for a variety of data types. The approximation may be especially useful in high-throughput applications in which a series of x-vectors is compared to one or more y-vectors.
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Authors who are presenting talks have a * after their name.
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