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Activity Number:
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356
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Type:
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Invited
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Date/Time:
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Wednesday, August 9, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economics Statistics Section
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| Abstract - #305178 |
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Title:
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Spatial Bayesian Variable Selection with Application to Functional Magnetic Resonance Imaging
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Author(s):
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Michael Smith*+ and Daniel Smith
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Companies:
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University of Sydney and University of Sydney
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Address:
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Econometrics and Business Statistics, Sydney, NSW 2006, Australia
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Keywords:
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binary MRF ; model averaging ; MCMC
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Abstract:
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In this talk a procedure to undertake Bayesian variable selection and model averaging for a series of regressions that are located on a lattice is proposed. For those regressors which are in common in the regressions, we consider using an Ising prior to smooth spatially the indicator variables representing whether or not the variable is zero or non-zero in each regression. This smoothes spatially the probabilities that each independent variable is non-zero in each regression, and indirectly smoothes spatially the regression coefficients. The approach is applied to the problem of functional magnetic resonance imaging in medical statistics, where massive datasets arise that need prompt processing. The relative strengths and weaknesses of using the Ising prior over alternative binary Markov random fields will also be discussed.
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