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Abstract Details

Activity Number: 291
Type: Topic Contributed
Date/Time: Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #307506
Title: Bayesian Smoothing Spline ANOVA Model
Author(s): Paul Speckman+ and Chin-I Cheng*
Companies: University of Missouri-Columbia and Central Michigan University
Address: Department of Statistics, University of Missouri, , Columbia, MO, 65211,
Keywords: smoothing spline ANOVA ; reproducing kernel ; additive model ; nonparametric
Abstract:

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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