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arXiv · 1711.01878

Modeling non-stationary extreme dependence with stationary max-stable processes and multidimensional scaling

Clément Chevalier·David Ginsbourger·Olivia Martius
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Abstract

Modeling the joint distribution of extreme weather events in multiple locations is a challenging task with important applications. In this study, we use max-stable models to study extreme daily precipitation events in Switzerland. The non-stationarity of the spatial process at hand involves important challenges, which are often dealt with by using a stationary model in a so-called climate space, with well-chosen covariates. Here, we instead chose to warp the weather stations under study in a latent space of higher dimension using multidimensional scaling (MDS). The advantage of this approach is its improved flexibility to reproduce highly non-stationary phenomena, while keeping a tractable stationary spatial model in the latent space. Two model fitting approaches, which both use MDS, are presented and compared to a classical approach that relies on composite likelihood maximization in a climate space. Results suggest that the proposed methods better reproduce the observed extremal coefficients and their complex spatial dependence.

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Clément Chevalier, David Ginsbourger, Olivia Martius. 2017-11-06. Modeling non-stationary extreme dependence with stationary max-stable processes and multidimensional scaling. https://arxiv.org/abs/1711.01878

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