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Bayesian and maximum likelihood methods for some two-segment generalized linear models.

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dc.contributor.advisor Seaman, John Weldon, 1956-
dc.contributor.author Miyamoto, Kazutoshi.
dc.contributor.other Baylor University. Dept. of Statistical Sciences. en
dc.date.copyright 2008-08
dc.identifier.uri http://hdl.handle.net/2104/5233
dc.description.abstract The change-point (CP) problem, wherein parameters of a model change abruptly at an unknown covariate value, is common in many fields, such as process control, epidemiology, and ecology. CP problems using two-segment regression models, such as those based on generalized linear models, are very flexible and widely used. For two-segment Poisson and logistic regression models, misclassification in the response is well known to cause attenuation of key parameters and other difficulties. How misclassification effects estimation of a CP in such models has not been studied. In this research, we consider the effect of misclassification on CP problems in Poisson and logistic regression. We focus on maximum likelihood and Bayesian methods. 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 librarywebmaster@baylor.edu for inquiries about permission. en
dc.subject Change-point problems. en
dc.subject Regression analysis. en
dc.subject Linear models (Statistics). en
dc.subject Bayesian statistical decision theory. en
dc.subject Mathematical statistics. en
dc.title Bayesian and maximum likelihood methods for some two-segment generalized linear models. en
dc.type Thesis en
dc.description.degree Ph.D. en
dc.rights.accessrights Baylor University access only en
dc.contributor.department Statistical Sciences. en


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