Modeling of spatial extremes in environmental data science: time to move away from max-stable processes
Environmental data science for spatial extremes has traditionally relied heavily on max-stable processes. Even though the popularity of these models has perhaps peaked with statisticians, they are still perceived and considered as the “state of the art” in many applied fields. However, while the asy...
Saved in:
Main Authors: | Raphaël Huser, Thomas Opitz, Jennifer L. Wadsworth |
---|---|
Format: | Article |
Language: | English |
Published: |
Cambridge University Press
2025-01-01
|
Series: | Environmental Data Science |
Subjects: | |
Online Access: | https://www.cambridge.org/core/product/identifier/S2634460224000542/type/journal_article |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
COMPARISON OF BLOCK MAXIMA AND PEAK OVER THRESHOLD METHODS FOR EXTREME RAINFALL MODELING OF DKI JAKARTA
by: Purnama Akbar
Published: (2024-12-01) -
A holistic stochastic model for precipitation events
by: Alexander Weyant, et al.
Published: (2025-02-01) -
Estimation des pluies journalières extrêmes supérieures à un seuil en climat tropical : cas de la Côte d'Ivoire
by: Gneneyougo Émile Soro, et al.
Published: (2016-08-01) -
Extreme PORT for Norwegian fire financial claims: Empirical assessment and financial VAR analysis
by: Abdussalam Aljadani
Published: (2024-12-01) -
Maximum lq-likelihood estimator of the heavy-tailed distribution parameter
by: Mohammed Ridha Kouider, et al.
Published: (2024-01-01)