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Activity Number:
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105
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 3, 2009 : 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 - #303469 |
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Title:
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Bayesian Variable Selection Methods Applied to Traffic Fatality Data
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Author(s):
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Gail Blattenberger*+ and Richard Fowles and Peter Loeb
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Companies:
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The University of Utah and The University of Utah and Rutgers University
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Address:
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1645 E Campus Central Drive, Salt Lake City, UT, 84112-9202,
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Keywords:
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BMA ; EBA ; SSVS ; MCMC ; Variable Selection
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Abstract:
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This paper looks at three Bayesian variable selection methods in the context of linear regression model specifications that simultaneously address issues related to model and parameter uncertainty. These methods are Extreme Bounds Analysis (Leamer), Bayesian Model Averaging (Raftery et al.), and Stochastic Search Variable Selection (George and McCulloch). The application relates motor vehicle traffic fatality rates in the United States.
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