(2008-10-14T20:38:46Z) Miyamoto, Kazutoshi.; Seaman, John Weldon, 1956-; Statistical Sciences.; Baylor University. Dept. of Statistical Sciences.

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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.