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Activity Number:
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151
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Type:
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Contributed
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Date/Time:
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Monday, August 7, 2006 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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| Abstract - #306327 |
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Title:
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Highest Posterior Model Selection
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Author(s):
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Tanujit Dey*+ and Hemant Ishwaran and J. S. Rao
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Companies:
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Case Western Reserve University and The Cleveland Clinic and Case Western Reserve University
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Address:
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323 Yost Hall Department of Statistics, Cleveland, OH, 44106-7054,
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Keywords:
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Abstract:
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We consider the properties of the highest posterior probability model in a linear regression setting. Under a spike and slab hierarchy we find that the highest posterior model is total risk consistent for model selection, but that it also possesses some curious properties. Most important of these is a marked underfitting in finite samples, a phenomenon well noted in the literature for Bayesian Information Criterion (BIC) related procedures, but not often associated with highest posterior model selection. We employ a rescaling of the hierarchy and show the resulting rescaled spike and slab models mitigate the effects of underfitting.
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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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