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Activity Number:
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27
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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| Abstract - #309590 |
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Title:
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On the Probability of Correct Selection for Large k Populations with Application to Microarray Data
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Author(s):
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Jason Wilson*+ and Xinping Cui
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Companies:
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University of California, Riverside and University of California, Riverside
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Address:
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Dept of Statistics, Riverside, CA, 92521,
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Keywords:
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Probability of Correct Selection ; Ranking and Selection ; Multiple Comparison ; Microarray ; d-best ; G-best
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Abstract:
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One frontier of modern statistical research is the "multiple comparison problem" (MCP) arising from data sets with large k (>1000) populations (e.g., microarrays and neuroimaging data). In this talk we demonstrate an alternative to hypothesis testing. It is an extension of the Probability of Correct Selection (PCS) concept, which avoids the MCP by its nature. The idea is to select the top t out of k populations and estimate the probability that the selection is correct, according to specified selection criteria. We propose "d-best" and "G-best" selection criteria that are suitable for large k problems and illustrate the application of the proposed method on two microarray data sets. Results show that our method is a powerful method for the purpose of selecting the "top t best" out of k populations.
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