B0516
Title: Conditional modelling of spatio-temporal sea surface temperature extremes, with high-dimensional inference using INLA
Authors: Emma Simpson - University College London (United Kingdom) [presenting]
Jenny Wadsworth - Lancaster University (United Kingdom)
Thomas Opitz - BioSP-INRAE (France)
Abstract: Recent extreme value theory literature has seen a significant emphasis on the modelling of spatial extremes, with comparatively little consideration of spatio-temporal extensions. This neglects an important feature of extreme events: their evolution over time. Many existing models for the spatial case are limited by the number of locations they can handle; this impedes extension to space-time settings, where models for higher dimensions are required. Moreover, the spatio-temporal models that do exist are restrictive in terms of the range of extremal dependence types they can capture. Recently, conditional approaches for studying multivariate and spatial extremes have been proposed, which enjoy benefits in terms of computational efficiency and an ability to capture both asymptotic dependence and asymptotic independence. We extend this class of models to a spatio-temporal setting, conditioning on the occurrence of an extreme value at a single space-time location. An application to modelling extreme Red Sea surface temperatures is considered, and Gaussian Markov random fields and the integrated nested Laplace approximation (INLA) are exploited to facilitate inference in higher dimensions.
