This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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122
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 2, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #306899 |
Title:
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Predictive Recursion: Convergence Theory, Extensions, and Applications
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Author(s):
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Ryan Martin*+ and Surya Tokdar
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Companies:
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Indiana University Purdue University Indianapolis and Duke University
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Address:
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402 N. Blackford St., Indianapolis, IN, 46202,
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Keywords:
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nonparametrics ;
mixture model ;
empirical Bayes ;
semiparametrics ;
stochastic approximation ;
density estimation
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Abstract:
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Predictive Recursion (PR) is a fast, recursive algorithm designed for nonparametric estimation of mixing distributions. I will present some new theory that describes the asymptotic behavior of PR estimates of the mixing distribution and mixture density under model mis-specification. In particular, the PR estimate of the mixture density converges almost surely in the total variation topology to the mixture which is closest, in a Kullback-Leibler sense, to the true data-generating density. Under extra conditions, results on the (minimax) rate of convergence as well as weak convergence of the PR estimate of the mixing distribution are available. The new asymptotic robustness results lead to an attractive construction of a semiparametric version of PR; details of this new algorithm will be given, along with some basic convergence theory. Empirical Bayes applications will be considered.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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