5 citations · 6 across the 4 of their papers we have counts for
4 papers · 1 filter
Gaussian mixture copulas for flexible dependence modelling in the body and tails of joint distributions
Lídia M. André, Jonathan A. Tawn
Fully describing the entire data set is essential in multivariate risk assessment, since moderate levels of one variable can influence another, potentially leading it to be extreme…
Neural Bayes estimation and selection for complex bivariate extremal dependence models
Lídia M. André, Jennifer L. Wadsworth, Raphaël Huser
Likelihood-free approaches are appealing for performing inference on complex dependence models, either because it is not possible to formulate a likelihood function, or its evaluat…
Extreme value methods for estimating rare events in Utopia
L. M. André, R. Campbell, E. D'Arcy +6
To capture the extremal behaviour of complex environmental phenomena in practice, flexi\-ble techniques for modelling tail behaviour are required. In this paper, we introduce a var…
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…