|A non-parametric entropy based approach to detect changes in climate extremes (joint work with A. Guillou and T. Riestch)|
Philippe Naveau, Laboratoire des Sciences du Climat et l'Environnement
Monday, February 17, 2014
4:00pm, room 223 Weber Building
In this talk, our goal is to provide and study a non-parametric estimator of the divergence for large excesses. Fundamental features in an extreme value analysis are captured by the tail behaviour. In its original form, the divergence is not expressed in terms of tails but in function of probability densities. One important aspect of this work is to propose an approximation of the divergence in terms of the tail distributions. This leads to a new non-parametric divergence estimator tailored for excesses. Its properties are studied. This application focuses primarily on temperature extremes measured at 24 european stations with at least 90 years of data. Here, the term extremes refers to rare excesses of daily maxima and minima. As we do not want to hypothesize any parametric form of such possible changes, we propose a new non-parametric estimator based on the Kullback-Leibler divergence tailored for extreme events. Our approach is also applied to seasonal extremes of daily maxima and minima for our 24 selected stations.