Bayesian approaches to parameter estimation and variable selection for misclassified binary data.

DSpace/Manakin Repository

BEARdocs is currently undergoing a scheduled upgrade. We expect the upgrade to be completed no later than Monday, March 2nd, 2015. During this time you will be able to access existing documents, but will not be able to log in or submit new documents.

Show simple item record

dc.contributor.advisor Stamey, James D. Beavers, Daniel.
dc.contributor.other Baylor University. Dept. of Statistical Sciences. en 2009-08
dc.description.abstract Binary misclassification is a common occurrence in statistical studies that, when ignored, induces bias in parameter estimates. The development of statistical methods to adjust for misclassification is necessary to allow for consistent estimation of parameters. In this work we develop a Bayesian framework for adjusting statistical models when fallible data collection methods produce misclassification of binary observations. In Chapter 2, we develop an approach for Bayesian variable selection for logistic regression models in which there exists a misclassified binary covariate. In this case, we require a subsample of gold standard validation data to estimate the sensitivity and specificity of the fallible classifier. In Chapter 3, we propose a Bayesian approach for the estimation of population prevalence of a biomarker in repeated diagnostic testing studies. In such situations, it is necessary to account for interindividual variability which we achieve through both the inclusion of random effects within logistic regression models and Bayesian hierarchical modeling. Our examples focus on applications for both reliability studies and biostatistical studies. Finally, we develop an approach to attempt to detect conditional dependence parameters between two fallible diagnostic tests for a binary logistic regression covariate in the absence of a gold standard test in Chapter 4. We compare the performance of the proposed procedure to previously published means assessing model fit. en
dc.rights Baylor University theses 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 for inquiries about permission. en
dc.subject Bayesian statistical decision theory. en
dc.subject Parameter estimation. en
dc.subject Error analysis (Mathematics) en
dc.subject Logistic regression analysis. en
dc.title Bayesian approaches to parameter estimation and variable selection for misclassified binary data. en
dc.type Thesis en Ph.D. en
dc.rights.accessrights Worldwide access. en
dc.rights.accessrights Access changed 10-31-11.
dc.contributor.department Statistical Sciences. en

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search BEARdocs

Advanced Search


My Account