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arXiv · 1401.0168

Efficient inference and simulation for elliptical Pareto processes

Emeric Thibaud·Thomas Opitz
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Abstract

Recent advances in extreme value theory have established $\ell$-Pareto processes as the natural limits for extreme events defined in terms of exceedances of a risk functional. Here we provide methods for the practical modelling of data based on a tractable yet flexible dependence model. We introduce the class of elliptical $\ell$-Pareto processes, which arise as the limit of threshold exceedances of certain elliptical processes characterized by a correlation function and a shape parameter. An efficient inference method based on maximizing a full likelihood with partial censoring is developed. Novel procedures for exact conditional and unconditional simulation are proposed. These ideas are illustrated using precipitation extremes in Switzerland.

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Emeric Thibaud, Thomas Opitz. 2015-11-11. Efficient inference and simulation for elliptical Pareto processes. https://doi.org/10.1093/biomet%2Fasv045

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