Spatial extreme quantile estimation using a weighted log-likelihood approach

Julie Carreau 1, 2 Stephane Girard 2 Eugen Ursu 2
2 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
NICDS Workshop on Statistical Methods for Geographic and Spatial Data in the Management of Natural Resources, Mar 2010, Montreal, Canada. pp.CDROM, 2010
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https://hal.archives-ouvertes.fr/hal-00762735
Contributeur : Stephane Girard <>
Soumis le : vendredi 7 décembre 2012 - 16:59:56
Dernière modification le : mercredi 11 avril 2018 - 01:58:58

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  • HAL Id : hal-00762735, version 1

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Julie Carreau, Stephane Girard, Eugen Ursu. Spatial extreme quantile estimation using a weighted log-likelihood approach. NICDS Workshop on Statistical Methods for Geographic and Spatial Data in the Management of Natural Resources, Mar 2010, Montreal, Canada. pp.CDROM, 2010. 〈hal-00762735〉

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