Regression models for change point data in extremes

Many extreme events are characterized by being always susceptible to outside influences that will modify their behavior at some point in time. The change point tool has been used in statistical models to detect when these changes occur. This paper presents a model based on a Bayesian approach that describes the behavior of extreme data regarding river quota, which may present more than one change point. In each one of the regimes, the GEV distribution is adjusted and each GEV parameter of each regime is written in function of presence of covariates. In the applications proposed here, the results showed that the model was able to accurately estimate the actual amount of change points in the series, and also showed that it was extremely important to consider them in the analysis, since it was verified that after the change of regime, the levels of return have changed considerably. The results were also able to show which months the occurrence of an extreme event is greater.