This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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403
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #306470 |
Title:
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An Adaptive Two-Step Procedure to Control Mean Number of False Discoveries
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Author(s):
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Dongmei Li*+
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Companies:
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The University of Hawaii at Manoa
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Address:
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1947 Young Street Apt.C, Honolulu, HI, 96826,
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Keywords:
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Bonferroni ;
Adaptive two-step procedure ;
Mean number of false discoveries
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Abstract:
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When Bonferroni procedure is used to control the mean number of false discoveries in microarray data analysis, it has an equivalent power and a superior stability in terms of the variance of both true discoveries and total discoveries compared to Benjamini-Hochberg procedure (Gordon et al., 2007). In this work, an adaptive two-step procedure developed from the Bonferroni procedure is present, which can control the mean number of false discoveries at a desired level. Compared to the Bonferroni procedure, simulation studies show that this adaptive two-step procedure has increased powers and decreased variances of both true discoveries and total discoveries. The above advantages of the adaptive two-step procedure increase as the proportions of true null hypotheses decrease under both independent and dependent cases.
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Authors who are presenting talks have a * after their name.
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