Bayesian adjustment for misclassification bias and prior elicitation for dependent parameters.

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dc.contributor.advisorSeaman, John Weldon, 1956-
dc.creatorLakshminarayanan, Divya Ranjani, 1993-
dc.date.accessioned2020-02-19T17:03:38Z
dc.date.available2020-02-19T17:03:38Z
dc.date.created2019-12
dc.date.issued2019-11-25
dc.date.submittedDecember 2019
dc.date.updated2020-02-19T17:03:38Z
dc.description.abstractThis research is motivated by problems in biopharmaceutical research. Prior elicitation is defined as formulating an expert's beliefs about one or more uncertain quantities into a joint probability distribution, and is often used in Bayesian statistics for specifying prior distributions for parameters in the data model. However, there is limited research on eliciting information about dependent random variables, which is often necessary in practice. We develop methods for constructing a prior distribution for the correlation coefficient using expert elicitation. Electronic health records are often used to assess potential adverse drug reaction risk, which may be misclassified for many reasons. Unbiased estimation with the presence of outcome misclassification requires additional information. Using internally validated data, we develop Bayesian models for analyzing misclassified data with a validation substudy and compare its performance to the existing frequentist approaches.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2104/10809
dc.language.isoen
dc.rights.accessrightsNo access-contact librarywebmaster@baylor.edu.
dc.subjectPrior elicitation. Misclassification. Bayesian.
dc.titleBayesian adjustment for misclassification bias and prior elicitation for dependent parameters.
dc.typeThesis
dc.type.materialtext
local.embargo.lift2024-12-01
local.embargo.terms2024-12-01
thesis.degree.departmentBaylor University. Dept. of Statistical Science.
thesis.degree.grantorBaylor University
thesis.degree.levelDoctoral
thesis.degree.namePh.D.

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