Bayesian approach to partially validated binary regression with response and exposure misclassification.

Simple item page
dc.contributor.advisorStamey, James D.
dc.creatorAnderson, Katrina Julie, 1987-
dc.creator.orcid0000-0002-3189-871X
dc.date.accessioned2018-09-07T13:57:29Z
dc.date.available2018-09-07T13:57:29Z
dc.date.created2018-08
dc.date.issued2018-06-09
dc.date.submittedAugust 2018
dc.date.updated2018-09-07T13:57:29Z
dc.description.abstractMisclassification of epidemiological and observational data is a problem that commonly arises and can have adverse ramifications on the validity of results if not properly handled. Considerable research has been conducted when only the response or only the exposure are misclassified, while less work has been done on the simultaneous case. We extend previous frequentist work by investigating a Bayesian approach to dependent, differential misclassification models. Using a logit model with misclassified binary response and exposure variables and assuming a validation sub-sample is available, we compare the resulting confidence and credible intervals under the two paradigms. We compare the results under varying percentages of validation subsamples, 100% (ideal scenario), 25%, 15%, 10%, 5%, 2.5%, and 0% (naive scenario) of the overall sample size. We extend this work further be examining scenarios for which the assumptions may falter; we assume independent, differential misclassification, increase the overall sample size, and vary the influence of our priors from diffuse to concentrated.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2104/10454
dc.language.isoen
dc.rights.accessrightsWorldwide access.
dc.rights.accessrightsAccess changed 12/14/20.
dc.subjectBayesian. Misclassification. Internal validation.
dc.titleBayesian approach to partially validated binary regression with response and exposure misclassification.
dc.typeThesis
dc.type.materialtext
local.embargo.lift2020-08-01
local.embargo.terms2020-08-01
thesis.degree.departmentBaylor University. Dept. of Statistical Science.
thesis.degree.grantorBaylor University
thesis.degree.levelDoctoral
thesis.degree.namePh.D.

Files

Original bundle

Now showing 1 - 2 of 2
Thumbnail Image
Name:
ANDERSON-DISSERTATION-2018.pdf
Size:
4.38 MB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
Katrina_Anderson_copyrightavailabilityform.pdf
Size:
780.63 KB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.95 KB
Format:
Plain Text
Description:
Download

Collections

Electronic Theses and Dissertations
Theses/Dissertations - Statistical Sciences