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...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Environmental Data Science |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2634460224000542/type/journal_article |
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| author | Raphaël Huser Thomas Opitz Jennifer L. Wadsworth |
| author_facet | Raphaël Huser Thomas Opitz Jennifer L. Wadsworth |
| author_sort | Raphaël Huser |
| collection | DOAJ |
| description | 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 asymptotic theory supporting the use of max-stable processes is mathematically rigorous and comprehensive, we think that it has also been overused, if not misused, in environmental applications, to the detriment of more purposeful and meticulously validated models. In this article, we review the main limitations of max-stable process models, and strongly argue against their systematic use in environmental studies. Alternative solutions based on more flexible frameworks using the exceedances of variables above appropriately chosen high thresholds are discussed, and an outlook on future research is given. We consider the opportunities offered by hybridizing machine learning with extreme-value statistics, highlighting seven key recommendations moving forward. |
| format | Article |
| id | doaj-art-d480b79e29b5458392c696e03d7de939 |
| institution | Kabale University |
| issn | 2634-4602 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Environmental Data Science |
| spelling | doaj-art-d480b79e29b5458392c696e03d7de9392025-01-16T21:50:57ZengCambridge University PressEnvironmental Data Science2634-46022025-01-01410.1017/eds.2024.54Modeling of spatial extremes in environmental data science: time to move away from max-stable processesRaphaël Huser0https://orcid.org/0000-0002-1228-2071Thomas Opitz1https://orcid.org/0000-0002-5863-5020Jennifer L. Wadsworth2https://orcid.org/0000-0003-2059-1234Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi ArabiaINRAE, Biostatistics and Spatial Processes (BioSP, UR546), Avignon, FranceSchool of Mathematical Sciences, Fylde College, Lancaster University, Lancaster, UKEnvironmental 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 asymptotic theory supporting the use of max-stable processes is mathematically rigorous and comprehensive, we think that it has also been overused, if not misused, in environmental applications, to the detriment of more purposeful and meticulously validated models. In this article, we review the main limitations of max-stable process models, and strongly argue against their systematic use in environmental studies. Alternative solutions based on more flexible frameworks using the exceedances of variables above appropriately chosen high thresholds are discussed, and an outlook on future research is given. We consider the opportunities offered by hybridizing machine learning with extreme-value statistics, highlighting seven key recommendations moving forward.https://www.cambridge.org/core/product/identifier/S2634460224000542/type/journal_articleartificial intelligenceblock maximaextreme-value theorymachine learningpeaks-over-threshold approachspatial processstochastic processtail dependence |
| spellingShingle | Raphaël Huser Thomas Opitz Jennifer L. Wadsworth Modeling of spatial extremes in environmental data science: time to move away from max-stable processes Environmental Data Science artificial intelligence block maxima extreme-value theory machine learning peaks-over-threshold approach spatial process stochastic process tail dependence |
| title | Modeling of spatial extremes in environmental data science: time to move away from max-stable processes |
| title_full | Modeling of spatial extremes in environmental data science: time to move away from max-stable processes |
| title_fullStr | Modeling of spatial extremes in environmental data science: time to move away from max-stable processes |
| title_full_unstemmed | Modeling of spatial extremes in environmental data science: time to move away from max-stable processes |
| title_short | Modeling of spatial extremes in environmental data science: time to move away from max-stable processes |
| title_sort | modeling of spatial extremes in environmental data science time to move away from max stable processes |
| topic | artificial intelligence block maxima extreme-value theory machine learning peaks-over-threshold approach spatial process stochastic process tail dependence |
| url | https://www.cambridge.org/core/product/identifier/S2634460224000542/type/journal_article |
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