7 papers · 1 filter
Separation-based causal discovery for extremes
Junshu Jiang, Jordan Richards, Raphaël Huser +1
Structural causal models (SCMs), with an underlying directed acyclic graph (DAG), provide a powerful analytical framework to describe the interaction mechanisms in large-scale comp…
Modeling Nonstationary Extremal Dependence via Deep Spatial Deformations
Xuanjie Shao, Jordan Richards, Raphael Huser
Modeling nonstationarity that often prevails in extremal dependence of spatial data can be challenging, and typically requires bespoke or complex spatial models that are difficult…
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Jordan Richards +1
Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner. Although they are appealing to use with spatial models, where es…
Neural Bayes estimators for censored inference with peaks-over-threshold models
Jordan Richards, Matthew Sainsbury-Dale, Andrew Zammit-Mangion +1
Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances i…
Modern extreme value statistics for Utopian extremes
Jordan Richards, Noura Alotaibi, Daniela Cisneros +4
Capturing the extremal behaviour of data often requires bespoke marginal and dependence models which are grounded in rigorous asymptotic theory, and hence provide reliable extrapol…
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…