65 citations · 240 across the 21 of their papers we have counts for
14 papers · 1 filter
Improving estimation for asymptotically independent bivariate extremes via global estimators for the angular dependence function
C. J. R. Murphy-Barltrop, J. L. Wadsworth, E. F. Eastoe
Modelling the extremal dependence of bivariate variables is important in a wide variety of practical applications, including environmental planning, catastrophe modelling and hydro…
Joint modelling of the body and tail of bivariate data
Lídia M. André, Jennifer L. Wadsworth, Adrian O'Hagan
In situations where both extreme and non-extreme data are of interest, modelling the whole data set accurately is important. In a univariate framework, modelling the bulk and tail…
Statistical inference for multivariate extremes via a geometric approach
Jennifer Wadsworth, Ryan Campbell
A geometric representation for multivariate extremes, based on the shapes of scaled sample clouds in light-tailed margins and their so-called limit sets, has recently been shown to…
Modeling spatial extremes using normal mean-variance mixtures
Zhongwei Zhang, Raphaël Huser, Thomas Opitz +1
Classical models for multivariate or spatial extremes are mainly based upon the asymptotically justified max-stable or generalized Pareto processes. These models are suitable when…
Spatial deformation for non-stationary extremal dependence
Jordan Richards, Jennifer L. Wadsworth
Modelling the extremal dependence structure of spatial data is considerably easier if that structure is stationary. However, for data observed over large or complicated domains, no…
High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields
Emma S. Simpson, Thomas Opitz, Jennifer L. Wadsworth
The conditional extremes framework allows for event-based stochastic modeling of dependent extremes, and has recently been extended to spatial and spatio-temporal settings. After s…