[HTML][HTML] Higher-dimensional spatial extremes via single-site conditioning
JL Wadsworth, JA Tawn - Spatial Statistics, 2022 - Elsevier
Currently available models for spatial extremes suffer either from inflexibility in the
dependence structures that they can capture, lack of scalability to high dimensions, or in …
dependence structures that they can capture, lack of scalability to high dimensions, or in …
A deep learning synthetic likelihood approximation of a non-stationary spatial model for extreme streamflow forecasting
R Majumder, BJ Reich - Spatial Statistics, 2023 - Elsevier
Extreme streamflow is a key indicator of flood risk, and quantifying the changes in its
distribution under non-stationary climate conditions is key to mitigating the impact of flooding …
distribution under non-stationary climate conditions is key to mitigating the impact of flooding …
Efficient modeling of spatial extremes over large geographical domains
Various natural phenomena exhibit spatial extremal dependence at short spatial distances.
However, existing models proposed in the spatial extremes literature often assume that …
However, existing models proposed in the spatial extremes literature often assume that …
Accounting for the spatial structure of weather systems in detected changes in precipitation extremes
The detection of changes over time in the distribution of precipitation extremes is
complicated by noise at the spatial scale of weather systems. Traditional approaches for …
complicated by noise at the spatial scale of weather systems. Traditional approaches for …
Flexible and efficient spatial extremes emulation via variational autoencoders
Many real-world processes have complex tail dependence structures that cannot be
characterized using classical Gaussian processes. More flexible spatial extremes models …
characterized using classical Gaussian processes. More flexible spatial extremes models …
High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields
The conditional extremes framework allows for event-based stochastic modeling of
dependent extremes, and has recently been extended to spatial and spatio-temporal …
dependent extremes, and has recently been extended to spatial and spatio-temporal …
Realistic and fast modeling of spatial extremes over large geographical domains
Various natural phenomena exhibit spatial extremal dependence at short distances only,
while it usually vanishes as the distance between sites increases arbitrarily. However …
while it usually vanishes as the distance between sites increases arbitrarily. However …
A flexible Bayesian hierarchical modeling framework for spatially dependent peaks-over-threshold data
In this work, we develop a constructive modeling framework for extreme threshold
exceedances in repeated observations of spatial fields, based on general product mixtures …
exceedances in repeated observations of spatial fields, based on general product mixtures …
Likelihood-free neural Bayes estimators for censored inference with peaks-over-threshold models
Inference for spatial extremal dependence models can be computationally burdensome in
moderate-to-high dimensions due to their reliance on intractable and/or censored …
moderate-to-high dimensions due to their reliance on intractable and/or censored …
Leveraging Extremal Dependence to Better Characterize the 2021 Pacific Northwest Heatwave
In late June, 2021, a devastating heatwave affected the US Pacific Northwest and western
Canada, breaking numerous all-time temperature records by large margins and directly …
Canada, breaking numerous all-time temperature records by large margins and directly …