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  • Modelling Spatial Extreme Events with Environmental Applications

    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, 28, 2018 DOI: 10.1016/j.spasta.2018.04.007

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Modelling spatial extreme events with environmental applications

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Modelling spatial extreme events with environmental applications. / Tawn, Jonathan Angus; Shooter, Robert; Towe, Ross Paul et al.
In: Spatial Statistics, Vol. 28, 12.2018, p. 39-58.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Tawn JA, Shooter R, Towe RP, Lamb R. Modelling spatial extreme events with environmental applications. Spatial Statistics. 2018 Dec;28:39-58. Epub 2018 May 4. doi: 10.1016/j.spasta.2018.04.007

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Bibtex

@article{f5a58ebce957467492389c456c4cc6de,
title = "Modelling spatial extreme events with environmental applications",
abstract = "Spatial extreme value analysis has been an area of rapid growth in the last decade. The focus has been on modelling the spatial componentwise maxima by max-stable processes. Here, we will explain the limitations of these modelling approaches and show how spatial models can be developed that overcome these deficiencies by exploiting the flexible conditional multivariate extremes models of Heffernan and Tawn (2004). We illustrate the benefits of these new spatial models through applications to North Sea wave analysis and to widespread UK river flood risk analysis. ",
keywords = "Conditional multivariate extreme values, Copula, Gaussian processes, Generalized Pareto distribution, Max-stable processes, Pareto processes, Spatial extremes",
author = "Tawn, {Jonathan Angus} and Robert Shooter and Towe, {Ross Paul} and Robert Lamb",
note = "This is the author{\textquoteright}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, 28, 2018 DOI: 10.1016/j.spasta.2018.04.007",
year = "2018",
month = dec,
doi = "10.1016/j.spasta.2018.04.007",
language = "English",
volume = "28",
pages = "39--58",
journal = "Spatial Statistics",
issn = "2211-6753",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Modelling spatial extreme events with environmental applications

AU - Tawn, Jonathan Angus

AU - Shooter, Robert

AU - Towe, Ross Paul

AU - Lamb, Robert

N1 - 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, 28, 2018 DOI: 10.1016/j.spasta.2018.04.007

PY - 2018/12

Y1 - 2018/12

N2 - Spatial extreme value analysis has been an area of rapid growth in the last decade. The focus has been on modelling the spatial componentwise maxima by max-stable processes. Here, we will explain the limitations of these modelling approaches and show how spatial models can be developed that overcome these deficiencies by exploiting the flexible conditional multivariate extremes models of Heffernan and Tawn (2004). We illustrate the benefits of these new spatial models through applications to North Sea wave analysis and to widespread UK river flood risk analysis.

AB - Spatial extreme value analysis has been an area of rapid growth in the last decade. The focus has been on modelling the spatial componentwise maxima by max-stable processes. Here, we will explain the limitations of these modelling approaches and show how spatial models can be developed that overcome these deficiencies by exploiting the flexible conditional multivariate extremes models of Heffernan and Tawn (2004). We illustrate the benefits of these new spatial models through applications to North Sea wave analysis and to widespread UK river flood risk analysis.

KW - Conditional multivariate extreme values

KW - Copula

KW - Gaussian processes

KW - Generalized Pareto distribution

KW - Max-stable processes

KW - Pareto processes

KW - Spatial extremes

U2 - 10.1016/j.spasta.2018.04.007

DO - 10.1016/j.spasta.2018.04.007

M3 - Journal article

VL - 28

SP - 39

EP - 58

JO - Spatial Statistics

JF - Spatial Statistics

SN - 2211-6753

ER -