Abstract Details
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Activity Number:
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140
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 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 - #309931 |
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Title:
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Bayesian Smoothing Spline ANOVA for Binary Response with Dimension Reduction
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Author(s):
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Chin-I Cheng*+ and Paul Speckman
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Companies:
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and Univ. of Missouri-Columbia
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Keywords:
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smoothing spline ANOVA ;
reproducing kernel ;
binary response ;
reduce dimension ;
scaled chi-squared prior
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Abstract:
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Smoothing spline ANOVA extends classical ANOVA models to model nonparametric functions with interaction effects. We generalize the model for binary response. The suitable priors are chosen for testing all of the components in the model. The dimension reduction is adapted to facilitate the computation. The effective computation enables us to obtain the Bayes factors for variable selection easily. The methods are illustrated using a real dataset collected from the Wisconsin Epidemiological Study of Diabetic Retinopathy.
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Authors who are presenting talks have a * after their name.
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