[DR15] Estimation of multivariate critical layers: Applications to hydrological data

Revue Internationale avec comité de lecture : Journal Journal de la Société Française de Statistique, pp. -, 2015
Résumé: Calculating return periods and critical layers (i.e. multivariate quantile curves) in a multivariate envi- ronment is a difficult problem. A possible consistent theoretical framework for the calculation of the return period, in a multi-dimensional environment, is essentially based on the notion of copula and level sets of the multivariate probability distribution. In this paper we propose a fast and parametric methodology to esti- mate the multivariate critical layers of a distribution and its associated return periods. The model is based on transformations of the marginal distributions and transformations of the dependence structure within the class of Archimedean copulas. The model has a tunable number of parameters, and we show that it is possible to get a competitive estimation without any global optimum research. We also get parametric ex- pressions for the critical layers and return periods. The methodology is illustrated on rainfall 5-dimensional real data. On this real data-set we obtain a good quality of estimation and we compare the obtained results with some classical parametric competitors. Finally we provide a simulation study.

Equipe: msdma
Collaboration: ISFA


@article {
title="{Estimation of multivariate critical layers: Applications to hydrological data}",
author="E. Di Bernardino and D. Rullière ",
journal=" Journal de la Société Française de Statistique",