Two new papers in Extremes

Extremes publishes original research on all aspects of statistical extreme value theory and its applications in science, engineering, economics, and other fields. It also presents authoritative reviews and case studies of theoretical advances and of extreme value methods and problems in important applied areas.

Two new papers accepted to the Extremes Special Issue on the EVA Data Competition:

  • Zhang, Z., Krainski, E., Zhong, P., Rue, H., and Huser, R. (2023+), Joint modeling and prediction of massive Spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach, Extremes, to appear [PDF preprint].
  • Cisneros, D., Gong, Y., Yadav, R., Hazra, A., and Huser, R. (2022+), A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes, Extremes, to appear [PDF preprint].


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