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

Date
2008-08
Authors
Miyamoto, Kazutoshi.
Access rights
Baylor University access only
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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.

Description
Includes bibliographical references (p.84-86)
Keywords
Change-point problems., Regression analysis., Linear models (Statistics)., Bayesian statistical decision theory., Mathematical statistics.
Citation