Abstract Details
Activity Number:
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629
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Mental Health Statistics Section
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Abstract - #308942 |
Title:
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Graphical Model--Based Multiple Testing with Applications to Genome-Wide Association Studies
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Author(s):
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Chunming Zhang*+ and Jie Liu and Page David
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Companies:
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University of Wisconsin - Madison and University of Wisconsin-Madison and University of Wisconsin-Madison
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Keywords:
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simultaneous inference ;
dependence ;
p-value ;
false discovery rate
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Abstract:
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Large-scale multiple testing tasks often exhibit dependence, and leveraging the dependence between individual tests is one challenging and important problem in statistics. We propose a multiple testing procedure based on a Markov-random-field-coupled mixture model. Simulation studies show that the numerical performance of multiple testing can be improved substantially by using our procedure. We apply the procedure to a real-world genome-wide association study on breast cancer, and we identify several SNPs with strong association evidence.
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Authors who are presenting talks have a * after their name.
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