Spatial kernel interpolation for annual rainfall maxima

Julie Carreau 1, 2 Stephane Girard 1 Eugen Ursu 1
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : We propose to estimate spatial extreme quantiles by a weighted log-likelihood approach. It is assumed that the conditional distribution of the variable of interest follows a generalized extreme-value distribution. The associated response surfaces are estimated thanks to the introduction of weights in the log-likelihood. These weights depend on the distance between the point of interest and the observations. The construction of a proper distance relies on the combination of a multidimensional scaling unfolding with a neural network regression. Our approach is illustrated both on simulated and real rainfall datasets.
Type de document :
Communication dans un congrès
WCRP-UNESCO 2010 - Workshop on metrics and methodologies of estimation of extreme climate events, Sep 2010, Paris, France. pp.CDROM, 2010
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https://hal.archives-ouvertes.fr/hal-00762731
Contributeur : Stephane Girard <>
Soumis le : vendredi 7 décembre 2012 - 16:56:41
Dernière modification le : mercredi 11 avril 2018 - 01:58:19

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  • HAL Id : hal-00762731, version 1

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Julie Carreau, Stephane Girard, Eugen Ursu. Spatial kernel interpolation for annual rainfall maxima. WCRP-UNESCO 2010 - Workshop on metrics and methodologies of estimation of extreme climate events, Sep 2010, Paris, France. pp.CDROM, 2010. 〈hal-00762731〉

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