This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 497
Type: Topic Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #307480
Title: Robustness of Nonparametric Bayesian Methods
Author(s): Steven MacEachern*+ and Antonio Lijoi
Companies: The Ohio State University and University of Pavia
Address: 404 Cockins Hall, Columbus, OH, 43210-1247,
Keywords: Dirichlet process ; Beta-Stacy process ; continuity
Abstract:

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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