218 citations · 323 across the 27 of their papers we have counts for
28 papers · 1 filter
Spatial Prediction with Guided Diffusion
Andrew Zammit-Mangion, Lachlan Astfalck, Raphaël Huser +2
Spatial prediction is a critically important task in data analyses that involve spatially or spatio-temporally-referenced data. It is commonly carried out using hierarchical models…
Neural Bayes estimation and selection for complex bivariate extremal dependence models
Lídia M. André, Jennifer L. Wadsworth, Raphaël Huser
Likelihood-free approaches are appealing for performing inference on complex dependence models, either because it is not possible to formulate a likelihood function, or its evaluat…
Modeling of spatial extremes in environmental data science: Time to move away from max-stable processes
Raphaël Huser, Thomas Opitz, Jennifer Wadsworth
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 statist…
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…
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…