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dc.contributor.advisorSun, Dongchuen_US
dc.contributor.authorLiang, Ye
dc.contributor.otherUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Dissertations. 2012 Dissertationsen_US
dc.date.issued2012
dc.date.submitted2012 Summeren_US
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on October 29, 2012).en_US
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.en_US
dc.descriptionDissertation advisor: Dr. Dongchu Sunen_US
dc.descriptionIncludes bibliographical references.en_US
dc.descriptionVita.en_US
dc.descriptionPh. D. University of Missouri--Columbia 2012.en_US
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Statistics.en_US
dc.description"July 2012"en_US
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.en_US
dc.format.extentvii, 89 pagesen_US
dc.identifier.otherLiangY-071912-D52
dc.identifier.urihttp://hdl.handle.net/10355/15884
dc.publisherUniversity of Missouri--Columbiaen_US
dc.relation.ispartof2012 Freely available dissertations (MU)en_US
dc.subjectBayesian statisticsen_US
dc.subjectspatial statisticsen_US
dc.subjectepidemiologyen_US
dc.subjectgraphical modelen_US
dc.titleBayesian methods on selected topicsen_US
dc.typeThesisen_US
thesis.degree.disciplineStatisticsen_US
thesis.degree.disciplineStatisticseng
thesis.degree.grantorUniversity of Missouri--Columbiaen_US
thesis.degree.levelDoctoralen_US
thesis.degree.namePh. D.en_US


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