Emma Simpson - Conditional modelling of spatio-temporal extremes with an application to high surface temperatures in the Red Sea
| Dates: | 2 February 2022 |
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| Times: | 14:00 - 15:00 |
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| What is it: | Seminar |
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| Organiser: | Department of Mathematics |
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| Who is it for: | University staff, External researchers, Current University students |
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| Speaker: | Emma Simpson |
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Emma Simpson, Lecturer in statistics at the Department of Statistical Science at University College London is our speaker for the Statistics seminar series.
Title: Conditional modelling of spatio-temporal extremes with an application to high surface temperatures in the Red Sea
Abstract: Recent extreme value theory literature has seen 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. I will first present an extension of this class of models to a spatio-temporal setting, conditioning on the occurrence of an extreme value at a single space-time location. I will then discuss two approaches for inference. The first is a composite likelihood approach which combines information from full likelihoods across multiple space-time conditioning locations, and is feasible for modelling hundreds of locations. The second involves taking a Bayesian perspective, with estimation implemented using the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) approach, with the possibility of handling several thousands of observation locations. I will discuss an application to modelling Red Sea surface temperatures, and show how the model can be used to assess the risk of coral bleaching attributed to high water temperatures over consecutive days.
Speaker
Emma Simpson
Organisation: University College London
Travel and Contact Information
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Zoom link: https://zoom.us/j/92947173491
