3 citations · 3 across the 4 of their papers we have counts for
4 papers
deepspat: An R package for modeling nonstationary spatial and spatio-temporal Gaussian and extremes data through deep deformations
Quan Vu, Xuanjie Shao, Raphaël Huser +1
Nonstationarity in spatial and spatio-temporal processes is ubiquitous in environmental datasets, but is not often addressed in practice, due to a scarcity of statistical software…
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…
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…