Recognizing a spatial extreme dependence structure: A deep learning approach-Reference-Cited by-同舟云学术

Recognizing a spatial extreme dependence structure: A deep learning approach

Published:2021-12-20 Issue: Volume: Page:
ISSN:1180-4009
Container-title:Environmetrics
language:en
Short-container-title:Environmetrics

Author:

Ahmed Manaf¹²,Maume‐Deschamps Véronique²,Ribereau Pierre²

Affiliation:

1. Department of Statistics and Informatics University of Mosul Mosul Iraq

2. Institut Camille Jordan ‐ ICJ Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208 Villeurbanne France

Funder

Agence Nationale de la Recherche

Publisher

Wiley

Subject

Ecological Modeling,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.2714

Reference40 articles.

1. Fitting spatial max‐mixture processes with unknown extremal dependence class: An exploratory analysis tool;Abu‐Awwad A.;Test,2020

2. Ahmed M. Maume‐Deschamps V. Ribereau P. &Vial C.(2017).A semi‐parametric estimation for max‐mixture spatial processes.arXivpreprint arXiv:1710.08120.

3. A flexible dependence model for spatial extremes;Bacro J.;Journal of Statistical Planning and Inference,2016

4. The multivariate gaussian tail model: An application to oceanographic data;Bortot P.;Journal of the Royal Statistical Society: Series C (Applied Statistics),2000

5. Extreme values of independent stochastic processes;Brown B. M.;Journal of Applied Probability,1977

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