Abstract Details
Activity Number:
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685
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Type:
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Contributed
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Date/Time:
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Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #310230 |
Title:
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Nonparametric Empirical Bayes and Variance Estimation
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Author(s):
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Marc Coram*+
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Companies:
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Stanford University
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Keywords:
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empirical Bayes ;
nonparametric ;
matrix-shaped ;
conditioning
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Abstract:
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We describe a nonparametric empirical Bayes method that extends the work of Johns 1986. This method can be applied to matrix-shaped data in order to improve estimates of the mean, the variance, or other statistics for each row of the matrix. The assumption is that there is an underlying distribution for each row, such that the observations in that row are iid realizations from that distribution. The distributions themselves need not have any known form but should be iid realizations from some unknown population of distributions. The estimates are further improved by using auxilliary covariates with one realization per row. Cross-validation is employed to select an appropriately rich model. Applications to large-scale testing of differential gene expression will be described.
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Authors who are presenting talks have a * after their name.
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