Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Ribereau, Pierre"'
This paper introduces a new methodology for extreme spatial dependence structure selection. It is based on deep learning techniques, specifically Convolutional Neural Networks -CNNs. Two schemes are considered: in the first scheme, the matching proba
Externí odkaz:
http://arxiv.org/abs/2409.13276
Publikováno v:
In Spatial Statistics December 2024 64
Publikováno v:
In Computational Statistics and Data Analysis August 2024 196
Understanding the behaviour of environmental extreme events is crucial for evaluating economic losses, assessing risks, health care and many other aspects. In the spatial context, relevant for environmental events, the dependence structure plays a ce
Externí odkaz:
http://arxiv.org/abs/2103.10739
Semiparametric estimation for space-time max-stable processes: F -madogram-based estimation approach
Max-stable processes have been expanded to quantify extremal dependence in spatio-temporal data. Due to the interaction between space and time, spatio-temporal data are often complex to analyze. So, characterizing these dependencies is one of the cru
Externí odkaz:
http://arxiv.org/abs/1905.07912
One of the main concerns in extreme value theory is to quantify the dependence between joint tails. Using stochastic processes that lack flexibility in the joint tail may lead to severe under-or over-estimation of probabilities associated to simultan
Externí odkaz:
http://arxiv.org/abs/1801.00981
Autor:
Blanchet-Scalliet, Christophette, Dorobantu, Diana, Gay, Laura, Maume-Deschamps, Véronique, Ribereau, Pierre
Publikováno v:
Stochastic Environmental Research and Risk Assessment, Springer Verlag (Germany), 2018, 32 (10), pp.2839 - 2848
This paper proposes a stochastic approach to model temperature dynamic and study related risk measures. The dynamic of temperatures can be modelled by a mean-reverting process such as an Ornstein-Uhlenbeck one. In this study, we estimate the paramete
Externí odkaz:
http://arxiv.org/abs/1710.04471
In this paper, we consider isotropic and stationary max-stable, inverse max-stable and max-mixture processes $X=(X(s))\_{s\in\bR^2}$ and the damage function $\cD\_X^{\nu}= |X|^\nu$ with $0<\nu<1/2$. We study the quantitative behavior of a risk measur
Externí odkaz:
http://arxiv.org/abs/1706.08244
Autor:
Khadraoui, Khader1 (AUTHOR) khader.khadraoui@mat.ulaval.ca, Ribereau, Pierre2 (AUTHOR)
Publikováno v:
Methodology & Computing in Applied Probability. Sep2019, Vol. 21 Issue 3, p765-788. 24p.
Semiparametric estimation for space-time max-stable processes: F -madogram-based estimation approach
Publikováno v:
Statistical Inference for Stochastic Processes
Statistical Inference for Stochastic Processes, Springer Verlag, 2021
Statistical Inference for Stochastic Processes, Springer Verlag, 2021
Max-stable processes have been expanded to quantify extremal dependence in spatio-temporal data. Due to the interaction between space and time, spatio-temporal data are often complex to analyze. So, characterizing these dependencies is one of the cru
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ecd2500ed9ef048292300c06bfe62fe
https://hal.archives-ouvertes.fr/hal-02133500
https://hal.archives-ouvertes.fr/hal-02133500