摘要：In many applications of lifetime data analysis, it is important to perform inferencesabout the change-point of the hazard function. The change-point could be a maximumfor unimodal hazard functions or a minimum for bathtub forms of hazard functionsand is usually of great interest in medical or industrial applications. For lifetime distri-butions where this change-point of the hazard function can be analytically calculated,its maximum likelihood estimator is easily obtained from the invariance properties ofthe maximum likelihoo d estimators. From the asymptotical normality of the max-imum likelihoo d estimators, confidence intervals can also be obtained. Consideringthe exponentiated Weibull distribution for the lifetime data, we have di.erent formsfor the hazard function: constant, increasing, unimodal, decreasing or bathtub forms.This model gives great .exibility of fit, but we do not have analytic expressions forthe change-point of the hazard function. In this way, we consider the use of MarkovChain Monte Carlo methods to get posterior summaries for the change-point of thehazard function considering the exponentiated Weibull distribution
关键词：change-point; exponentiated Weibull distribution; hazard function; lifetime data anal-;ysis; Markov Chain Monte Carlo
