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

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dc.contributor.advisor Stamey, James D. Powers, Stephanie L.
dc.contributor.other Baylor University. Dept. of Statistical Sciences. en 2009-05
dc.description.abstract 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. 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 Regression analysis. en
dc.subject Missing observations (Statistics). en
dc.subject Error analysis (Mathematics). en
dc.subject Poisson distribution. en
dc.title Bayesian approach to inference and variable selection for misclassified and under-reported response models. en
dc.type Thesis en Ph.D. en
dc.rights.accessrights Baylor University access only en
dc.contributor.department Statistical Sciences. en

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