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Abstract Details
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Activity Number:
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26
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #301800 |
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Title:
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Spectral Analysis of Variance Models: A Bayesian Nonparametric Approach
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Author(s):
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Christian Macaro*+ and Raquel Prado
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Companies:
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SAS Institute Inc. and University of California at Santa Cruz
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Address:
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1028 oberlin rd, raleigh, NC, 27605,
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Keywords:
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Spectral analysis of variance ;
Bayesian nonparametrics ;
Whittle's approximation ;
Bernstein-Dirichlet priors
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Abstract:
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The factorial analysis of variance models in the frequency domain is considered. Specifically, the Whittle's approximation to the likelihood function and a Bayesian nonparametric approach provide posterior inference based on Bernstein-Dirichlet prior distributions. The prior is strategically important as it carries identifiability conditions for the models and quantifies the degree of confidence in such conditions. A MCMC Metropolis-Hastings algorithm for posterior inference is presented. The approach is illustrated by analyzing simulated and real data. In particular, a functional magnetic resonance (fMRI) brain analysis is presented where responses are measured in individuals who participated in a designed experiment.
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