A semi‐parametric stochastic generator for bivariate extreme events
Abstract
The analysis of multiple extreme values aims to describe the stochastic behaviour of observations in the joint upper tail of a distribution function. For instance, being able to simulate multivariate extreme events is convenient for end users who need a large number of random replications of extremes as input of a given complex system to test its sensitivity. The simulation of multivariate extremes is often based on the assumption that the dependence structure, the so‐called extremal dependence function, is described by a specific parametric model. We propose a simulation method for sampling bivariate extremes, under the assumption that the extremal dependence function is semi‐parametric. This yields a flexible tool that can be broadly applied in real‐data analyses. With the aim of estimating the probability of belonging to some extreme sets, our methodology is examined via simulation and illustrated by an analysis of strong wind gusts in France. Copyright © 2017 John Wiley & Sons, Ltd.
Citing Literature
Number of times cited according to CrossRef: 3
- Boris Beranger, Simone A. Padoan, Scott A. Sisson, Estimation and uncertainty quantification for extreme quantile regions, Extremes, 10.1007/s10687-019-00364-0, (2019).
- Daniel Cooley, Emeric Thibaud, Federico Castillo, Michael F. Wehner, A nonparametric method for producing isolines of bivariate exceedance probabilities, Extremes, 10.1007/s10687-019-00348-0, (2019).
- Elena Di Bernardino, Clémentine Prieur, Estimation of the multivariate conditional tail expectation for extreme risk levels: Illustration on environmental data sets, Environmetrics, 10.1002/env.2510, 29, 7, (2018).
