Change Point Problems in the Model of Logistic Regression

The paper considers generalized maximum likelihood asymptotic power one tests which aim to detect a change point in logistic regression when the alternative specifies that a change occurred in parameters of the model. A guaranteed nonasymptotic upper bound for the significance level of each of the tests is presented. For cases in which the test supports the conclusion that there was a change point, we propose a maximum likelihood estimator of that point and present results regarding the asymptotic properties of the estimator. An important field of application of this approach is occupational medicine, where for a lot chemical compounds and other agents, so-called threshold limit values (or TLVs) are specified. We demonstrate applications of the test and the maximum likelihood estimation of the change point using an actual problem that was encountered with real data. AMS 2000 subject classifications. Primary 62F03, 62H15, 62F10; Secondary 62F05, 62L10.