Modern extreme value statistics for Utopian extremes
Abstract
Capturing the extremal behaviour of data often requires bespoke marginal and dependence models which are grounded in rigorous asymptotic theory, and hence provide reliable extrapolation into the upper tails of the data-generating distribution. We present a toolbox of four methodological frameworks, motivated by modern extreme value theory, that can be used to accurately estimate extreme exceedance probabilities or the corresponding level in either a univariate or multivariate setting. Our frameworks were used to facilitate the winning contribution of Team Yalla to the EVA (2023) Conference Data Challenge, which was organised for the 13 International Conference on Extreme Value Analysis. This competition comprised seven teams competing across four separate sub-challenges, with each requiring the modelling of data simulated from known, yet highly complex, statistical distributions, and extrapolation far beyond the range of the available samples in order to predict probabilities of extreme events. Data were constructed to be representative of real environmental data, sampled from the fantasy country of "Utopia"
Keywords
Cite
@article{arxiv.2311.11054,
title = {Modern extreme value statistics for Utopian extremes},
author = {Jordan Richards and Noura Alotaibi and Daniela Cisneros and Yan Gong and Matheus B. Guerrero and Paolo Redondo and Xuanjie Shao},
journal= {arXiv preprint arXiv:2311.11054},
year = {2024}
}