Model selection in logistic joinpoint regression with applications to analyzing cohort mortality patterns

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Abstract

We consider a general model for anomaly detection in a longitudinal cohort mortality pattern based on logistic joinpoint regression with unknown joinpoints. We discuss backward and forward sequential procedures for selecting both the locations and the number of joinpoints. Estimation of the model parameters and the selection algorithms are illustrated with longitudinal data on cancer mortality in a cohort of chemical workers. Copyright © 2007 John Wiley & Sons, Ltd.

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