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dc.contributor.advisorSpeckman, Paul L. (Paul Lorenz), 1946-eng
dc.contributor.advisorCheng, Chin-Ieng
dc.contributor.authorCheng, Chin-Ieng
dc.date.issued2009eng
dc.date.submitted2009 Summereng
dc.descriptionThe entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file.eng
dc.descriptionDissertation advisor: Dr. Paul Speckman.eng
dc.descriptionVitaeng
dc.descriptionPh. D. University of Missouri--Columbia 2009eng
dc.description.abstractBased on the pioneering work by Wahba (1990) in smoothing splines for nonparametric regression, Gu (2002) decomposed the regression function based on a tensor sum decomposition of inner product spaces into orthogonal subspaces so the estimated functions from each subspaces can be viewed separately. This is based on an ANOVA type decomposition and is called the smoothing spline ANOVA (SSANOVA) model. Current research related to smoothing spline ANOVA focuses on the frequentist approach for statistical inference in estimation and prediction. In this dissertation, we apply a fully Bayesian approach in SSANOVA to extend statistical inference not only for estimation and prediction but to model testing and selection. The prior selected for the smoothing parameter in level effects is a variant of the Zellner-Siow prior. Two sets of priors, the Pareto and the scaled [superscript x]2/1, are used for the smoothing parameters corresponding to smooth effects. We study this fully Bayesian SSANOVA model for Gaussian response variables and also extend it to generalized additive models with binary response variables.eng
dc.format.extentxii, 107 pageseng
dc.identifier.oclc695994050eng
dc.identifier.urihttps://hdl.handle.net/10355/10357
dc.identifier.urihttps://doi.org/10.32469/10355/10357eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subject.lcshSmoothing (Statistics)eng
dc.subject.lcshNonparametric statisticseng
dc.subject.lcshRegression analysiseng
dc.subject.lcshBayesian statistical decision theoryeng
dc.subject.lcshGaussian processeseng
dc.titleBayesian smoothing spline analysis of variance modelseng
dc.typeThesiseng
thesis.degree.disciplineStatistics (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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