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dc.contributor.advisorSun, Dongchueng
dc.contributor.authorLiu, Yajuneng
dc.contributor.otherUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Dissertations. 2012 Dissertationseng
dc.date.issued2012eng
dc.date.issued2012eng
dc.date.submitted2012 Summereng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on October 29, 2012).eng
dc.descriptionThe entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.eng
dc.descriptionDissertation advisor: Dr. Dongchu Suneng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionVita.eng
dc.descriptionPh. D. University of Missouri--Columbia 2012.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Statistics.eng
dc.description"July 2012"eng
dc.description.abstractThis dissertation discusses the methodologies of applying Bayesian hierarchical models to different data with geographical characteristics or with right-censored failure time. A conditional autoregressive (CAR) prior is used for the model to capture spatial effects. Markov chain Monte Carlo (MCMC) methods are used in the sampling. The Ancillary-Sufficient Interweaving Strategy (ASIS) is applied to improve the performance for some parameters. The convergence of some of the parameters improved greatly, but the others do not have very significant improvement. However, the overall performance has improved greatly since it needs much fewer iterations than using regular Gibbs sampling to achieve convergence. For the survival analysis, we propose a generalized linear mixed model with different effects for the hazard rates, and adopte a cure rate model in Chen et al. (1999) for the hazards. A ratio-of-uniforms method is used to get the posterior density of some parameters that can not be simply sampled by common methods. Both the Weibull model and cure rate models are compared. Moreover, for the same data set, competing risks model is considered by incorporating spatial effect to a latent competing risk model from Gelfand et al. (2000). The sampling method mentioned in Berger & Sun (1993) is adapted for efficiency. Finally, spatial confounding occurs when incorporating spatial effects in a regression model. Several estimators of the coefficients are compared for their Mean Squared Errors. The corresponding prediction errors are also discussed.eng
dc.format.extentx, 132 pageseng
dc.identifier.oclc872569057eng
dc.identifier.otherLiuY-072012-D20eng
dc.identifier.urihttp://hdl.handle.net/10355/15886eng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartof2012 Freely available dissertations (MU)eng
dc.subjectBayesian statisticseng
dc.subjectspatial analysiseng
dc.subjectratios-of-uniformseng
dc.subjectsurvival analysiseng
dc.subjectspatial confoundingeng
dc.titleBayesian analysis of spatial and survival models with applications of computation techniqueseng
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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