1 citations · 2 across the 7 of their papers we have counts for
8 papers · 1 filter
Extreme quantile regression with deep learning
Jordan Richards, Raphaël Huser
Estimation of extreme conditional quantiles is often required for risk assessment of natural hazards in climate and geo-environmental sciences and for quantitative risk management…
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…
Max-convolution processes with random shape indicator kernels
Pavel Krupskii, Raphaël Huser
In this paper, we introduce a new class of models for spatial data obtained from max-convolution processes based on indicator kernels with random shape. We show that this class of…
A Neural Network-Based Approach to Normality Testing for Dependent Data
Minwoo Kim, Marc G Genton, Raphael Huser +1
There is a wide availability of methods for testing normality under the assumption of independent and identically distributed data. When data are dependent in space and/or time, ho…
Spatial wildfire risk modeling using mixtures of tree-based multivariate Pareto distributions
Daniela Cisneros, Arnab Hazra, Raphaël Huser
Wildfires pose a severe threat to the ecosystem and economy, and risk assessment is typically based on fire danger indices such as the McArthur Forest Fire Danger Index (FFDI) used…
A flexible Bayesian hierarchical modeling framework for spatially dependent peaks-over-threshold data
Rishikesh Yadav, Raphaël Huser, Thomas Opitz
In this work, we develop a constructive modeling framework for extreme threshold exceedances in repeated observations of spatial fields, based on general product mixtures of random…