Estimation of multivariate critical layers: Applications to rainfall data

Elena Di Bernardino () and Didier Rulliere ()
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Elena Di Bernardino: CEDRIC - Centre d'études et de recherche en informatique et communications - ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise - CNAM - Conservatoire National des Arts et Métiers [CNAM]

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Abstract: Calculating return periods and critical layers (i.e., multivariate quantile curves) in a multivariate environment is a di cult 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 estimate 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 expressions for the critical layers and return periods. The methodology is illustrated on hydrological 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

Keywords: Multivariate probability transformations; level sets estimation; copulas; hyperbolic conversion functions; risk assessment; multivariate return periods (search for similar items in EconPapers)
Date: 2015-01
New Economics Papers: this item is included in nep-ecm
Note: View the original document on HAL open archive server: https://hal.science/hal-00940089v3
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Citations: View citations in EconPapers (2)

Published in Journal de la Société Française de Statistique, 2015, 156 (1), pp. 11-50

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