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pro vyhledávání: '"de Fondeville, Raphaël"'
Autor:
Aymon, Damien, Lam, Dan-Thuy, Marti, Lancelot, Maury-Laribière, Pauline, Choirat, Christine, de Fondeville, Raphaël
Public services collect massive volumes of data to fulfill their missions. These data fuel the generation of regional, national, and international statistics across various sectors. However, their immense potential remains largely untapped due to str
Externí odkaz:
http://arxiv.org/abs/2406.17087
The Sihl river, located near the city of Zurich in Switzerland, is under continuous and tight surveillance as it flows directly under the city's main railway station. To issue early warnings and conduct accurate risk quantification, a dense network o
Externí odkaz:
http://arxiv.org/abs/2106.13077
In 2019, Eliud Kipchoge ran a sub-two hour marathon wearing Nike's Alphafly shoes. Despite being the fastest marathon time ever recorded, it wasn't officially recognized as race conditions were tightly controlled to maximize his success. Besides, Kip
Externí odkaz:
http://arxiv.org/abs/2104.08509
Peaks-over-threshold analysis using the generalized Pareto distribution is widely applied in modelling tails of univariate random variables, but much information may be lost when complex extreme events are studied using univariate results. In this pa
Externí odkaz:
http://arxiv.org/abs/2002.02711
Publikováno v:
In International Journal of Forecasting July-September 2023 39(3):1448-1459
Verifying probabilistic forecasts for extreme events is a highly active research area because popular media and public opinions are naturally focused on extreme events, and biased conclusions are readily made. In this context, classical verification
Externí odkaz:
http://arxiv.org/abs/1905.04022
The distribution of spatially aggregated data from a stochastic process $X$ may exhibit a different tail behavior than its marginal distributions. For a large class of aggregating functionals $\ell$ we introduce the $\ell$-extremal coefficient that q
Externí odkaz:
http://arxiv.org/abs/1712.09816
Max-stable processes are increasingly widely used for modelling complex extreme events, but existing fitting methods are computationally demanding, limiting applications to a few dozen variables. $r$-Pareto processes are mathematically simpler and ha
Externí odkaz:
http://arxiv.org/abs/1605.08558
Akademický článek
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Autor:
de Fondeville, Raphaël1 (AUTHOR), Davison, Anthony C.1 (AUTHOR) anthony.davison@epfl.ch
Publikováno v:
Journal of the Royal Statistical Society: Series B (Statistical Methodology). Sep2022, Vol. 84 Issue 4, p1392-1422. 31p.