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Activity Number:
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607
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305233 |
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Title:
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A General Framework for Multiple Testing Dependence
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Author(s):
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Jeffrey Leek*+ and John Storey
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Companies:
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Johns Hopkins University and Princeton University
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Address:
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550 North Broadway, Suite 1111 , Baltimore, MD, 21205,
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Keywords:
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Dependence Kernel ; False Discovery Rate ; High-Dimensional Data ; Multiple Testing Dependence ; Surrogate Variable Analysis
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Abstract:
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I will present a general framework for performing large-scale significance testing in the presence of arbitrarily strong dependence. We have derived a low-dimensional set of random vectors, called a dependence kernel, that fully captures the dependence structure in an observed high-dimensional data set. This result is a surprising reversal of the "curse of dimensionality" in the high-dimensional hypothesis testing setting. We have shown theoretically that conditioning on a dependence kernel is sufficient to render statistical tests independent regardless of the level of dependence in the observed data. This framework for multiple testing dependence has implications in a variety of common multiple testing problems, such as in gene expression studies, brain imaging, and spatial epidemiology.
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- Authors who are presenting talks have a * after their name.
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