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Activity Number: 685
Type: Contributed
Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #310230
Title: Nonparametric Empirical Bayes and Variance Estimation
Author(s): Marc Coram*+
Companies: Stanford University
Keywords: empirical Bayes ; nonparametric ; matrix-shaped ; conditioning
Abstract:

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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