Modeling extreme events: Univariate and multivariate data-driven approaches

Article Article scientifique Article Modeling extreme events: Univariate and multivariate data-driven approaches eng 2024-10-16 2025-03 author Buritica Borda Gloria Patricia 990975 author Hentschel Manuel Jan 969331 Recherche et enseignement GSEM (RESEM) author Pasche Olivier Colin 0000-0002-1202-9199 949999 Recherche et enseignement GSEM (RESEM) author Roettger Frank 946396 author Zhang Zhongwei 403316 75-99 10.1007/s10687-024-00499-9 1386-1999 https://link.springer.com/10.1007/s10687-024-00499-9 This article summarizes the contribution of team genEVA to the EVA (2023) Conference Data Challenge. The challenge comprises four individual tasks, with two focused on univariate extremes and two related to multivariate extremes. In the first univariate assignment, we estimate a conditional extremal quantile using a quantile regression approach with neural networks. For the second, we develop a fine-tuning procedure for improved extremal quantile estimation with a given conservative loss function. In the first multivariate sub-challenge, we approximate the data-generating process with a copula model. In the remaining task, we use clustering to separate a high-dimensional problem into approximately independent components. Overall, competitive results were achieved for all challenges, and our approaches for the univariate tasks yielded the most accurate quantile estimates in the competition. EVA (2023) Conference Data Challenge Extreme value theory Extreme quantile regression Extremal dependence Dewey 310 117 Research Center for Statistics S5 false Extremes 28 1 5e201f08-5f22-469a-91b4-b44ab2c087ea Article (Published version) Public Article_scientifique_from_crossref.pdf 1024279 application/pdf 8f30c063b3f46e807c55dad6e4eb787315d1de243e86740b2a089f64e4c41f6c openAccess Faculté d'économie et de management / Research Center for Statistics

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Modeling extreme events: Univariate and multivariate data-driven approaches	Extremes			2025	5	231

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