Recognizing a spatial extreme dependence structure: a deep learning approach
From MaRDI portal
Publication:6626446
DOI10.1002/ENV.2714zbMATH Open1545.62696MaRDI QIDQ6626446
Manaf Ahmed, Véronique Maume-Deschamps, Pierre Ribereau
Publication date: 28 October 2024
Published in: Environmetrics (Search for Journal in Brave)
Cites Work
- A flexible dependence model for spatial extremes
- Extremal \(t\) processes: elliptical domain of attraction and a spectral representation
- Stationary max-stable fields associated to negative definite functions
- A spectral representation for max-stable processes
- Neocognition: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
- Models for stationary max-stable random fields
- Editorial: EVA 2019 data competition on spatio-temporal prediction of Red Sea surface temperature extremes
- Fitting spatial max-mixture processes with unknown extremal dependence class: an exploratory analysis tool
- On the block maxima method in extreme value theory: PWM estimators
- Spatial extremes: models for the stationary case
- Dependence modelling for spatial extremes
- Modelling pairwise dependence of maxima in space
- Statistics for near independence in multivariate extreme values
- Extreme values of independent stochastic processes
- A dependence measure for multivariate and spatial extreme values: Properties and inference
- Models and inference for uncertainty in extremal dependence
- Modeling Spatial Extremes via Ensemble-of-Trees of Pairwise Copulas
- Deep Neural Networks in a Mathematical Framework
- The Multivariate Gaussian Tail Model: An Application to Oceanographic Data
- Determining the dependence structure of multivariate extremes
- Dependence measures for extreme value analyses
This page was built for publication: Recognizing a spatial extreme dependence structure: a deep learning approach
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6626446)
