This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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291
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 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 - #307506 |
Title:
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Bayesian Smoothing Spline ANOVA Model
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Author(s):
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Paul Speckman+ and Chin-I Cheng*
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Companies:
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University of Missouri-Columbia and Central Michigan University
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Address:
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Department of Statistics, University of Missouri, , Columbia, MO, 65211,
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Keywords:
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smoothing spline ANOVA ;
reproducing kernel ;
additive model ;
nonparametric
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Abstract:
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Smoothing spline ANOVA (SSANOVA) is a nonparametric function estimate based on an ANOVA type of decomposition. Gu (2002) decomposed the regression function based on a tensor sum decomposition of inner product spaces into orthogonal subspaces. In this talk, we introduce appropriate priors to enable a fully Bayesian approach in SSANOVA. This approach facilitates Bayesian inference on all parts of the model. We study the fully Bayesian SSANOVA model for Gaussian response variables. A mixed model application is illustrated using a real dataset from an experiment measuring potassium in dogs.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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