Topics in Bayesian sample size determination and Bayesian model selection.

This dissertation contains three topics using the Bayesian paradigm for statistical inference. The first topic is related to Bayesian sample size determination with a misclassified prevalence variable when two possibly dependent diagnostic tests are used for estimation. After accounting for the dependence structure, the required sample size will be larger than that assuming independence between the tests. The second topic is also concerned with Bayesian sample size calculation with a misclassified binary response variable. Differing from the first topic, an error-free covariate is added. Simulations demonstrate that choices of prior distributions have a great impact on the resultant sample size. The last topic is about Bayesian variable selection under the multiple regression model. Two competing Bayesian methods are Bayesian model averaging and reversible jump MCMC. It is found that reversible jump MCMC, expected to give better models, does not seem to differ from Bayesian model averaging in examples considered.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Doctoral

Grantor

Baylor University.

Year dc:date.issued

2007

Author dc:creator

• Cheng, Dunlei.

Advisor dc:contributor.advisor

• Stamey, James D.

• Bayesian statistical decision theory.
• Sampling (Statistics)

Statement dc:rights

• Baylor University works 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 libraryquestions@baylor.edu for inquiries about permission.

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2104/5039

OAI identifier oai:identifier

oai:baylor-ir.tdl.org:2104/5039