Efficient inference and simulation for elliptical Pareto processes

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.

References in corpus (3)

• Statistical Modeling of Spatial Extremes
• Stationary max-stable fields associated to negative definite functions
• Estimation of Huesler-Reiss distributions and Brown-Resnick processes

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