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Activity Number:
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244
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #308560 |
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Title:
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Empirical Bayes Methods for Controlling the False Discovery Rate with Dependent Data
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Author(s):
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Weihua Tang*+ and Cun-Hui Zhang
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Companies:
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Bristol-Myers Squibb Company and Rutgers University
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Address:
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506 Dillion Court, North Brunswick, NJ, 08902,
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Keywords:
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multiple testing ; false discovery rate ; empirical bayes ; dependent data
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Abstract:
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False discovery rate (FDR) has been widely used as an error measure in large scale multiple testing problems, but most research in the area has been focused on procedures for controlling the FDR based on independent test statistics or the properties of such procedures for test statistics with certain types of stochastic dependence. Based on an approach proposed in Tang and Zhang (2005), we further develop Empirical Bayes methods for controlling the FDR with dependent data. We implement our methodology in a time series model and report the results of a simulation study which demonstrate the advantages of proposed Empirical Bayes approach. This is joint work with Cun-Hui Zhang.
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