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Activity Number:
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278
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 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 - #303523 |
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Title:
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A Note on the Bayes Factor in a Semiparametric Regression Model
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Author(s):
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Taeryon Choi*+ and Jaeyong Lee and Anindya Roy
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Companies:
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Inha University and Seoul National University and University of Maryland, Baltimore County
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Address:
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Yonghyung dong 253, Incheon, International, 402-751, South Korea
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Keywords:
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Bayes factor ; consistency ; semiparametric ; regression model
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Abstract:
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In this paper, we consider a semiparametric regression model where the unknown regression function is the sum of parametric and nonparametric parts. The parametric part is a finite dimensional multiple regression function whereas the nonparametric part is represented by an infinite series of orthogonal basis. In this model, we investigate the large sample property of the Bayes factor for testing the parametric null model against the semiparametric alternative model. Under some conditions on the prior and design matrix, we identify the analytic form of the Bayes factor and show that the Bayes factor is consistent, i.e. converges to infinity in probability under the parametric null model, while converges to zero under the semiparametric alternative, as the sample size increases.
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