{"id":{"repo_id":"baylor","oai_identifier":"oai:baylor-ir.tdl.org:2104/13133"},"canonical_url":"https://search.dev.ndltd.org/etd/baylor/oai:baylor-ir.tdl.org:2104/13133","repository":{"repo_id":"baylor","name":"Baylor University","base_url":"https://baylor-ir.tdl.org/server/oai/request"},"display":{"title":"Bayesian approaches to problems in diagnostic testing and underreported count data.","abstract":"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.","abstract_html":"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. 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