A marginal modelling approach for predicting wildfire extremes across the contiguous United States

DOI10.1007/s10687-023-00469-7arXiv2112.15372OpenAlexW4362590456MaRDI QIDQ6100565

Rob Shooter, Callum J. R. Murphy-Barltrop, Emma S. Simpson, Eleanor D'Arcy

Publication date: 12 May 2023

Published in: Extremes (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2112.15372

zbMATH Keywords

extreme value theorywildfire predictionsemi-parametric modelling

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Residual life time at great age
• Statistical inference using extreme order statistics
• The MELBS team winning entry for the EVA2017 competition for spatiotemporal prediction of extreme rainfall using generalized extreme value quantiles
• Modelling extremes of spatial aggregates of precipitation using conditional methods
• Spatiotemporal wildfire modeling through point processes with moderate and extreme marks
• Generalized Additive Models for Exceedances of High Thresholds With an Application to Return Level Estimation for U.S. Wind Gusts
• Data Mining and Knowledge Discovery Handbook
• An introduction to statistical modeling of extreme values
• Dependence measures for extreme value analyses
• Reconstruction of incomplete wildfire data using deep generative models
• Gradient boosting with extreme-value theory for wildfire prediction
• A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes

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