A refined extreme quantiles estimator of Weibull tail-distributions
| Autor: | El Methni, Jonathan, Girard, Stéphane |
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| Přispěvatelé: | Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension (STATIFY), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Chaire Stress Test - BNP Paribas/Ecole polytechnique/Fondation de l'X., ANR-15-IDEX-0002,UGA,IDEX UGA(2015) |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | 13th International Conference on Extreme Value Analysis, Probabilistic and Statistical Models and their Applications 13th International Conference on Extreme Value Analysis, Probabilistic and Statistical Models and their Applications, Jun 2023, Milan, Italy CMStatistics 2022-15th International Conference of the ERCIM WG on Computational and Methodological Statistics CMStatistics 2022-15th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2022, London, United Kingdom |
| Popis: | International audience; In the case of Weibull tail distributions, the most commonly used methodology for estimating extreme quantiles is based on two estimators: an order statistic to estimate an intermediate quantile and an estimator of the Weibull tail coefficient. The common practice is to select the same intermediate sequence for both estimators. We show how an adapted choice of two different intermediate sequences leads to a reduction of the asymptotic bias associated with the resulting refined estimator. The asymptotic normality of the latter estimator is established, and a data-driven method is introduced for the practical selection of the intermediate sequences. Our approach is compared to various bias-reduced estimators in a simulation study. An illustration on real data is also provided. |
| Databáze: | OpenAIRE |
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