Topics in Bayesian models with ordered parameters : response misclassification, covariate misclassification, and sample size determination.

dc.contributor.advisorSeaman, John Weldon, 1956-
dc.creatorTecson, Kristen M., 1989-
dc.date.accessioned2015-09-04T14:00:37Z
dc.date.available2015-09-04T14:00:37Z
dc.date.created2015-08
dc.date.issued2015-06-30
dc.date.submittedAugust 2015
dc.date.updated2015-09-04T14:00:37Z
dc.description.abstractResearchers often analyze data assuming models with constrained parameters. Order constrained parameters are of particular interest. In this dissertation, we examine three Bayesian models which incorporate ordered parameters. We investigate ordered differential response misclassification in a logistic regression model and provide an adjustment for it using a conditional prior structure. We examine a parametric Bayesian Weibull proportional hazards model with ordered covariate misclassification and provide an adjustment for it. Finally, we consider informative hypotheses (Hoijtink, 2012) and perform sample size determination for this problem using the two priors approach of Brutti et al. (2008).
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2104/9477
dc.language.isoen
dc.rights.accessrightsWorldwide access.
dc.rights.accessrightsAccess changed 12/14/20.
dc.subjectBayesian. Constrained parameters.
dc.titleTopics in Bayesian models with ordered parameters : response misclassification, covariate misclassification, and sample size determination.
dc.typeThesis
dc.type.materialtext
local.embargo.lift2020-08-01
local.embargo.terms2020-08-01
thesis.degree.departmentBaylor University. Dept. of Statistical Sciences.
thesis.degree.grantorBaylor University
thesis.degree.levelDoctoral
thesis.degree.namePh.D.

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