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    Rights statement: This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 51, 100677, 2022 DOI: 10.1016/j.spasta.2022.100677

    Accepted author manuscript, 4.13 MB, PDF document

    Embargo ends: 14/06/23

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

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Higher-dimensional spatial extremes via single-site conditioning

Research output: Contribution to Journal/MagazineJournal articlepeer-review

E-pub ahead of print
Article number100677
<mark>Journal publication date</mark>30/06/2022
<mark>Journal</mark>Spatial Statistics
Volume51
Number of pages31
Publication StatusE-pub ahead of print
Early online date30/06/22
<mark>Original language</mark>English

Abstract

Currently available models for spatial extremes suffer either from inflexibility in the dependence structures that they can capture, lack of scalability to high dimensions, or in most cases, both of these. We present an approach to spatial extreme value theory based on the conditional multivariate extreme value model, whereby the limit theory is formed through conditioning upon the value at a particular site being extreme. The ensuing methodology allows for a flexible class of dependence structures, as well as models that can be fitted in high dimensions. To overcome issues of conditioning on a single site, we suggest a joint inference scheme based on all observation locations, and implement an importance sampling algorithm to provide spatial realizations and estimates of quantities conditioning upon the process being extreme at any of one of an arbitrary set of locations. The modelling approach is applied to Australian summer temperature extremes, permitting assessment of the spatial extent of high temperature events over the continent.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 51, 100677, 2022 DOI: 10.1016/j.spasta.2022.100677