Bayesian methods on selected topics

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Bayesian methods on selected topics

Please use this identifier to cite or link to this item: http://hdl.handle.net/10355/15884

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dc.contributor.advisor Sun, Dongchu en_US
dc.contributor.author Liang, Ye
dc.contributor.other University of Missouri-Columbia. Graduate School. Theses and Dissertations. Dissertations. 2012 Dissertations en_US
dc.date.accessioned 2012-10-29T17:54:31Z
dc.date.available 2012-10-29T17:54:31Z
dc.date.issued 2012
dc.date.submitted 2012 Summer en_US
dc.identifier.other LiangY-071912-D52
dc.identifier.uri http://hdl.handle.net/10355/15884
dc.description Title from PDF of title page (University of Missouri--Columbia, viewed on October 29, 2012). en_US
dc.description The 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.description Dissertation advisor: Dr. Dongchu Sun en_US
dc.description Includes bibliographical references. en_US
dc.description Vita. en_US
dc.description Ph. D. University of Missouri--Columbia 2012. en_US
dc.description Dissertations, Academic -- University of Missouri--Columbia -- Statistics. en_US
dc.description "July 2012" en_US
dc.description.abstract Bayesian 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.extent vii, 89 pages en_US
dc.language.iso en_US en_US
dc.publisher University of Missouri--Columbia en_US
dc.relation.ispartof 2012 Freely available dissertations (MU) en_US
dc.subject Bayesian statistics en_US
dc.subject spatial statistics en_US
dc.subject epidemiology en_US
dc.subject graphical model en_US
dc.title Bayesian methods on selected topics en_US
dc.type Thesis en_US
thesis.degree.discipline Statistics en_US
thesis.degree.grantor University of Missouri--Columbia en_US
thesis.degree.name Ph. D. en_US
thesis.degree.level Doctoral en_US


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