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

dc.contributor.advisorSeaman, John Weldon, 1956-
dc.contributor.authorMiyamoto, Kazutoshi.
dc.contributor.departmentStatistical Sciences.en
dc.contributor.otherBaylor University. Dept. of Statistical Sciences.en
dc.date.accessioned2008-10-14T20:38:46Z
dc.date.accessioned2017-04-07T19:33:18Z
dc.date.available2008-10-14T20:38:46Z
dc.date.available2017-04-07T19:33:18Z
dc.date.copyright2008-08
dc.date.issued2008-10-14T20:38:46Z
dc.descriptionIncludes bibliographical references (p.84-86)en
dc.description.abstractThe 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.description.degreePh.D.en
dc.description.statementofresponsibilityby Kazutoshi Miyamoto.en
dc.format.extentxi, 86 p. : ill.en
dc.format.extent538676 bytes
dc.format.extent154640 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2104/5233
dc.language.isoen_USen
dc.rightsBaylor 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.rights.accessrightsBaylor University access onlyen
dc.subjectChange-point problems.en
dc.subjectRegression analysis.en
dc.subjectLinear models (Statistics).en
dc.subjectBayesian statistical decision theory.en
dc.subjectMathematical statistics.en
dc.titleBayesian and maximum likelihood methods for some two-segment generalized linear models.en
dc.typeThesisen

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