Bayesian approach to inference and variable selection for misclassified and under-reported response models.

Stamey, James D.Powers, Stephanie L.Baylor University. Dept. of Statistical Sciences.2009-07-012009-07-012009-052009-07-01http://hdl.handle.net/2104/5355Includes bibliographical references (p. 175-178).Response partial missingness is a problem in studies conducted in a variety of disciplines. We investigate the impact ignoring response partial missingness has on determining a subset of significant covariates in non-linear regression. In particular, we consider non-differential misclassification in logistic regression and non-differential under-reporting in Poisson regression. Differential misclassification and differential under-reporting are also addressed but in less detail. We then develop a Bayesian approach to select significant covariates while accounting for the partial missingness. Examples of response partial missingness in which the variable selection method is applied include determining the factors that contribute to whether or not an individual will stop smoking and how many days an individual is absent from work.xv, 178 p. : ill.43516031 bytes158501 bytesapplication/pdfapplication/pdfen-USBaylor 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 librarywebmaster@baylor.edu for inquiries about permission.Bayesian statistical decision theory.Regression analysis.Missing observations (Statistics).Error analysis (Mathematics).Poisson distribution.Bayesian approach to inference and variable selection for misclassified and under-reported response models.ThesisBaylor University access only