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Research output: Contribution to Journal/Magazine › Comment/debate › peer-review
Research output: Contribution to Journal/Magazine › Comment/debate › peer-review
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TY - JOUR
T1 - Modeling of spatial extremes in environmental data science
T2 - Time to move away from max-stable processes
AU - Huser, Raphael
AU - Opitz, Thomas
AU - Wadsworth, Jennifer
PY - 2025/1/15
Y1 - 2025/1/15
N2 - 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.
AB - 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.
U2 - 10.1017/eds.2024.54
DO - 10.1017/eds.2024.54
M3 - Comment/debate
VL - 4
JO - Environmental Data Science
JF - Environmental Data Science
IS - 1
M1 - e3
ER -