This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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302
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #306683 |
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Title:
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MCMC Model Composition Strategy in a Hierarchical Setting
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Author(s):
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Susan Simmons*+ and Qijun Fang and Fang Fang and Ann Stapleton and Karl Ricanek
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Companies:
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University of North Carolina Wilmington and The University of Arizona and The University of Arizona and University of North Carolina Wilmington and University of North Carolina Wilmington
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Address:
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601 South College Road, Wilmington, NC, 28403,
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Keywords:
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Markov Chain Monte Carlo ;
Hierarchical model ;
QTL
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Abstract:
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Bayesian hierarchical models have been shown to be useful in modeling complex data. In the Bayesian hierarchical setting when there are many parameters, it is of interest to understand which parameters are important in the model. We discuss using the Markov chain Monte Carlo model composition strategy to identify important parameters in a Bayesian hierarchical setting. The methodology is tested through a rigorous simulation study and applied to a plant QTL data set.
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