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December 2016 Bayes Factors for Smoothing Spline ANOVA
Chin-I. Cheng, Paul L. Speckman
Bayesian Anal. 11(4): 957-975 (December 2016). DOI: 10.1214/15-BA974

Abstract

This paper describes an approach for variable selection and hypothesis testing in semiparametric additive models using Bayes factors in smoothing spline analysis of variance (SSANOVA) models. Effects can be linear or nonparametric (i.e., smooth or interactions between selected linear and smooth effects). To evaluate the importance of each term in the model, we develop Bayes factors for both linear and nonparametric terms. We compute approximate Bayes factors by Monte Carlo and Laplace integration. These Bayes factors can be computed to compare any two sub-models including one model nested in another. This permits formal tests of any portion or simultaneous portions of an SSANOVA model. We demonstrate this approach with an example.

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Chin-I. Cheng. Paul L. Speckman. "Bayes Factors for Smoothing Spline ANOVA." Bayesian Anal. 11 (4) 957 - 975, December 2016. https://doi.org/10.1214/15-BA974

Information

Published: December 2016
First available in Project Euclid: 12 October 2015

zbMATH: 1357.62254
MathSciNet: MR3545470
Digital Object Identifier: 10.1214/15-BA974

Rights: Copyright © 2016 International Society for Bayesian Analysis

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Vol.11 • No. 4 • December 2016
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