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dc.contributor.advisorStamey, James D.
dc.contributor.authorYuan, Jiang, 1984-
dc.date.accessioned2014-01-28T16:05:13Z
dc.date.available2014-01-28T16:05:13Z
dc.date.copyright2013-12
dc.date.issued2014-01-28
dc.identifier.urihttp://hdl.handle.net/2104/8926
dc.description.abstractBayesian sample size determination can be computationally intensive for mod- els where Markov chain Monte Carlo (MCMC) methods are commonly used for in- ference. It is also common in a large database where the unmeasured confounding presents. We present a normal theory approximation as an alternative to the time consuming MCMC simulations in sample size determination for a binary regression with unmeasured confounding. Cheng et al. (2009) develop a Bayesian approach to average power calculations in binary regression models. They then apply the model to the common medical scenario where a patient's disease status is not known. In this dissertation, we generate simulations based on their Bayesian model with both binary and normal outcomes. We also use normal theory approximation to speed up such sample size determination and compare power and computational time for both.en_US
dc.language.isoen_USen_US
dc.publisheren
dc.rightsBaylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission.en_US
dc.subjectBayesian statistical decision theory.en_US
dc.subjectMonte Carlo method.en_US
dc.subjectMarkov processes.en_US
dc.titleNormal approximation for Bayesian models with non-sampling bias.en_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.rights.accessrightsWorldwide access.en_US
dc.rights.accessrightsAccess changed 6/7/19.
dc.contributor.departmentStatistical Sciences.en_US
dc.contributor.schoolsBaylor University. Dept. of Statistical Sciences.en_US


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