This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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497
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #307480 |
Title:
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Robustness of Nonparametric Bayesian Methods
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Author(s):
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Steven MacEachern*+ and Antonio Lijoi
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Companies:
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The Ohio State University and University of Pavia
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Address:
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404 Cockins Hall, Columbus, OH, 43210-1247,
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Keywords:
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Dirichlet process ;
Beta-Stacy process ;
continuity
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Abstract:
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Nonparametric Bayesian methods provide a means of flexibly modelling data. They have been used successfully for problems ranging from exploratory data analysis to sharp, focused inference in sophisticated hierarchical models. They now come in a wide variety of forms: One is able to capture both a single nonparametric distribution and a collection of nonparametric distributions, whether the collection be finite, countable or uncountable. An oft-touted benefit of the methods is their robustness to a violation of parametric assumptions. While the models can fit data quite well, their very flexibility can render these fits non-robust. This talk provides an overview of situations where the methods have shown a lack of robustness. The mechanisms which lead to the lack of robustness are described, concepts are formalized, and results given.
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