Modeling Extreme Events: Univariate and Multivariate Data-Driven Approaches
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917835661377536 |
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| author | Buriticá, Gloria Hentschel, Manuel Pasche, Olivier C. Röttger, Frank Zhang, Zhongwei |
| author_facet | Buriticá, Gloria Hentschel, Manuel Pasche, Olivier C. Röttger, Frank Zhang, Zhongwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14910 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modeling Extreme Events: Univariate and Multivariate Data-Driven Approaches Buriticá, Gloria Hentschel, Manuel Pasche, Olivier C. Röttger, Frank Zhang, Zhongwei Methodology Applications 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. |
| title | Modeling Extreme Events: Univariate and Multivariate Data-Driven Approaches |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2401.14910 |