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dc.contributor.advisorSun, Dongchueng
dc.contributor.authorLiang, Yeeng
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.abstractBayesian methods are widely adopted nowadays in statistical analysis. It is especially useful for the statistical inference of complex models or hierarchical models, for which the frequentist methods are usually difficult to be applied. Though as a decision-making theory, often there are debates on the prior choices, the Bayesian methods benefits from its computational feasibility, with a variety of Markov chain Monte Carlo algorithms available. Three topics are studied using Bayesian methods. First, the competing risks model for masked failure data is investigated, which suffers from an identification problem. The identification problem and possible solutions are discussed and a Bayesian framework is used for the complex model. The other two topics are relevant, focusing on the lattice system and areal data. For a specific lattice system called generative star-shape model, objective priors are developed in order to achieve better estimations. The last topic is modeling areal data from a special project. A hierarchical model is developed for modeling the bounded outcomes with spatial variation and a Bayesian analysis is performed.eng
dc.format.extentvii, 89 pageseng
dc.identifier.oclc872569003eng
dc.identifier.otherLiangY-071912-D52eng
dc.identifier.urihttp://hdl.handle.net/10355/15884eng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartof2012 Freely available dissertations (MU)eng
dc.subjectBayesian statisticseng
dc.subjectspatial statisticseng
dc.subjectepidemiologyeng
dc.subjectgraphical modeleng
dc.titleBayesian methods on selected topicseng
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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