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

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Doctoral

Grantor

Baylor University.

Year dc:date.issued

2008

Author dc:creator

• Miyamoto, Kazutoshi.

Advisor dc:contributor.advisor

• Seaman, John Weldon, Jr., 1956-

• Change-point problems.
• Regression analysis.
• Linear models (Statistics)
• Bayesian statistical decision theory.
• Mathematical statistics.

Statement dc:rights

• Baylor University works 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 libraryquestions@baylor.edu for inquiries about permission.

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2104/5233

OAI identifier oai:identifier

oai:baylor-ir.tdl.org:2104/5233