Bayesian approaches to problems in diagnostic testing and underreported count data.

This dissertation is a compilation of three chapters pertaining to three distinct topics. The first chapter describes Bayesian joint estimation for predictive values of continuous diagnostic tests. We find that allowing for uncertainty in disease prevalence is essential for producing unbiased estimation regions. In the second chapter, we assess the effect of state-level immigration policy on intrastate migration utilizing four types of statistical models. These models are compared using cross-validation. Finally, the last chapter extends a zero-inflated Poisson distribution accounting for underreporting into the Bayesian paradigm. We analyze this Bayesian model in multiple simulation studies. When underreporting is inaccurately estimated, model parameter estimation can be biased. Placing a prior on the reporting probability in cases of uncertainty leads to more accurate parameter estimation.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Doctoral

Grantor

Baylor University.

Year dc:date.issued

2024

Author dc:creator

• Roberman, Jamie L., 1997-

Advisor dc:contributor.advisor

• Stamey, James D.

• Underreporting.
• Zero-inflation.
• Count data.
• Predictive values.
• Joint interval estimation.
• Misclassification.
• Case control.
• Gravity models.
• Spatially weighted covariates.

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/2104/13133

OAI identifier oai:identifier

oai:baylor-ir.tdl.org:2104/13133