Bayesian inference on mixture models and their applications

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dc.contributor.advisorCalvin, James A.
dc.contributor.advisorMallick, Bani K.
dc.contributor.committeeMemberFan, Ruzong
dc.contributor.committeeMemberSchlumprecht, Thomas
dc.creatorChang, Ilsung
dc.date.accessioned2006-08-16T19:12:40Z
dc.date.available2006-08-16T19:12:40Z
dc.date.created2003-05
dc.date.issued2006-08-16
dc.description.abstractMixture models are useful in describing a wide variety of random phenomena because of their flexibility in modeling. They have continued to receive increasing attention over the years from both a practical and theoretical point of view. In their applications, estimating the number of mixture components is often the main research objective or the first step toward it. Estimation of the number of mixture components heavily depends on the underlying distribution. As an extension of normal mixture models, we introduce a skew-normal mixture model and adapt the reversible jump Markov chain Monte Carlo algorithm to estimate the number of components with some applications to biological data. The reversible jump algorithm is also applied to the Cox proportional hazard model with frailty. We consider a regression model for the variance components in the proportional hazards frailty model. We propose a Bayesian model averaging procedure with a reversible jump Markov chain Monte Carlo step which selects the model automatically. The resulting regression coefficient estimates ignore the model uncertainty from the frailty distribution. Finally, the proposed model and the estimation procedure are illustrated with simulated example and real data.en
dc.format.digitalOriginborn digitalen
dc.format.extent2504542 bytesen
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/1969.1/3990
dc.language.isoen_US
dc.publisherTexas A&M University
dc.subjectMixture modelen
dc.subjectSkew-normal distributionen
dc.titleBayesian inference on mixture models and their applicationsen
dc.typeBooken
dc.typeThesisen
dc.type.genreElectronic Dissertationen
dc.type.materialtexten
thesis.degree.departmentStatisticsen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen

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