Abstract Details
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Activity Number:
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290
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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Abstract #312264
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Title:
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A Nonparametric Likelihood Ratio Test for Detecting Sparse Normal Mixtures
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Author(s):
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Wenhua Jiang*+ and Cun-Hui Zhang
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Companies:
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and Rutgers University
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Keywords:
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empirical Bayes ;
sparse normal means ;
multiple testing ;
convex optimization
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Abstract:
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We will review the general maximum likelihood empirical Bayes (GMLEB) method for the estimation of a mean vector in the Gaussian sequence model. Then we consider the generalized MLE in testing problem. Specifically, we consider the problem of detecting sparse heterogeneous normal mixtures. We demonstrate the power of the proposed method by numerical experiments. Some theory will also be presented.
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Authors who are presenting talks have a * after their name.
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