218 citations · 323 across the 27 of their papers we have counts for
10 papers · 1 filter
Patterns in Spatio-Temporal Extremes
Marco Oesting, Raphaël Huser
In environmental science applications, extreme events frequently exhibit a complex spatio-temporal structure, which is difficult to describe flexibly and estimate in a computationa…
Spatial modeling and future projection of extreme precipitation extents
Peng Zhong, Manuela Brunner, Thomas Opitz +1
Extreme precipitation events with large spatial extents may have more severe impacts than localized events as they can lead to widespread flooding. It is debated how climate change…
Insights into the drivers and spatio-temporal trends of extreme Mediterranean wildfires with statistical deep-learning
Jordan Richards, Raphaël Huser, Emanuele Bevacqua +1
Extreme wildfires are a significant cause of human death and biodiversity destruction within countries that encompass the Mediterranean Basin. Recent worrying trends in wildfire ac…
Partial Tail-Correlation Coefficient Applied to Extremal-Network Learning
Yan Gong, Peng Zhong, Thomas Opitz +1
We propose a novel extremal dependence measure called the partial tail-correlation coefficient (PTCC), in analogy to the partial correlation coefficient in classical multivariate a…
Flexible Modeling of Nonstationary Extremal Dependence using Spatially-Fused LASSO and Ridge Penalties
Xuanjie Shao, Arnab Hazra, Jordan Richards +1
Statistical modeling of a nonstationary spatial extremal dependence structure is challenging. Max-stable processes are common choices for modeling spatially-indexed block maxima, w…
Likelihood-Free Parameter Estimation with Neural Bayes Estimators
Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Raphaël Huser
Neural point estimators are neural networks that map data to parameter point estimates. They are fast, likelihood free and, due to their amortised nature, amenable to fast bootstra…