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pro vyhledávání: '"62g32"'
Autor:
Naveau, Philippe, Segers, Johan
When passing from the univariate to the multivariate setting, modelling extremes becomes much more intricate. In this introductory exposition, classical multivariate extreme value theory is presented from the point of view of multivariate excesses ov
Externí odkaz:
http://arxiv.org/abs/2412.18477
This paper presents a novel deep learning framework for estimating multivariate joint extremes of metocean variables, based on the Semi-Parametric Angular-Radial (SPAR) model. When considered in polar coordinates, the problem of modelling multivariat
Externí odkaz:
http://arxiv.org/abs/2412.15808
Autor:
Lhaut, Stéphane, Segers, Johan
The angular measure on the unit sphere characterizes the first-order dependence structure of the components of a random vector in extreme regions and is defined in terms of standardized margins. Its statistical recovery is an important step in learni
Externí odkaz:
http://arxiv.org/abs/2411.12673
Autor:
Wan, Phyllis
Quantifying the risks of extreme scenarios requires understanding the tail behaviours of variables of interest. While the tails of individual variables can be characterized parametrically, the extremal dependence across variables can be complex and i
Externí odkaz:
http://arxiv.org/abs/2411.00573
Autor:
Girard, Stéphane, Pakzad, Cambyse
We propose an extreme dimension reduction method extending the Extreme-PLS approach to the case where the covariate lies in a possibly infinite-dimensional Hilbert space. The ideas are partly borrowed from both Partial Least-Squares and Sliced Invers
Externí odkaz:
http://arxiv.org/abs/2410.05517
Autor:
Osorio-Marulanda, Pablo A., Ramirez, John Esteban Castro, Jiménez, Mikel Hernández, Reyes, Nicolas Moreno, Unanue, Gorka Epelde
Creation of synthetic data models has represented a significant advancement across diverse scientific fields, but this technology also brings important privacy considerations for users. This work focuses on enhancing a non-parametric copula-based syn
Externí odkaz:
http://arxiv.org/abs/2409.18611
Autor:
Butsch, Lucas, Fasen-Hartmann, Vicky
In multivariate extreme value analysis, the estimation of the dependence structure in extremes is a challenging task, especially in the context of high-dimensional data. Therefore, a common approach is to reduce the model dimension by considering onl
Externí odkaz:
http://arxiv.org/abs/2409.10174
Understanding the tail behavior of distributions is crucial in statistical theory. For instance, the tail of a distribution plays a ubiquitous role in extreme value statistics, where it is associated with the likelihood of extreme events. There are s
Externí odkaz:
http://arxiv.org/abs/2409.06308
Autor:
Bücher, Axel, Staud, Torben
The block maxima method is a standard approach for analyzing the extremal behavior of a potentially multivariate time series. It has recently been found that the classical approach based on disjoint block maxima may be universally improved by conside
Externí odkaz:
http://arxiv.org/abs/2409.05529
Autor:
Reinbott, Felix, Janßen, Anja
Principal component analysis (PCA) is one of the most popular dimension reduction techniques in statistics and is especially powerful when a multivariate distribution is concentrated near a lower-dimensional subspace. Multivariate extreme value distr
Externí odkaz:
http://arxiv.org/abs/2408.10650