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New estimation methods for extremal bivariate return curves

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New estimation methods for extremal bivariate return curves. / Murphy-Barltrop, Callum; Wadsworth, Jennifer; Eastoe, Emma.
In: Environmetrics, Vol. 34, No. 5, e2797, 31.08.2023.

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Murphy-Barltrop C, Wadsworth J, Eastoe E. New estimation methods for extremal bivariate return curves. Environmetrics. 2023 Aug 31;34(5):e2797. Epub 2023 Feb 17. doi: 10.1002/env.2797

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@article{ab118969f0784a44955b08e7ed21dfc4,
title = "New estimation methods for extremal bivariate return curves",
abstract = "In the multivariate setting, estimates of extremal risk measures are important in many contexts, such as environmental planning and structural engineering. In this paper, we propose new estimation methods for extremal bivariate return curves, a risk measure that is the natural bivariate extension to a return level. Unlike several existing techniques, our estimates are based on bivariate extreme value models that can capture both key forms of extremal dependence. We devise tools for validating return curve estimates, as well as representing their uncertainty, and compare a selection of curve estimation techniques through simulation studies. We apply the methodology to two met-ocean data sets, with diagnostics indicatinggenerally good performance.",
keywords = "dependence modelling, extremes, risk measure",
author = "Callum Murphy-Barltrop and Jennifer Wadsworth and Emma Eastoe",
year = "2023",
month = aug,
day = "31",
doi = "10.1002/env.2797",
language = "English",
volume = "34",
journal = "Environmetrics",
issn = "1180-4009",
publisher = "John Wiley and Sons Ltd",
number = "5",

}

RIS

TY - JOUR

T1 - New estimation methods for extremal bivariate return curves

AU - Murphy-Barltrop, Callum

AU - Wadsworth, Jennifer

AU - Eastoe, Emma

PY - 2023/8/31

Y1 - 2023/8/31

N2 - In the multivariate setting, estimates of extremal risk measures are important in many contexts, such as environmental planning and structural engineering. In this paper, we propose new estimation methods for extremal bivariate return curves, a risk measure that is the natural bivariate extension to a return level. Unlike several existing techniques, our estimates are based on bivariate extreme value models that can capture both key forms of extremal dependence. We devise tools for validating return curve estimates, as well as representing their uncertainty, and compare a selection of curve estimation techniques through simulation studies. We apply the methodology to two met-ocean data sets, with diagnostics indicatinggenerally good performance.

AB - In the multivariate setting, estimates of extremal risk measures are important in many contexts, such as environmental planning and structural engineering. In this paper, we propose new estimation methods for extremal bivariate return curves, a risk measure that is the natural bivariate extension to a return level. Unlike several existing techniques, our estimates are based on bivariate extreme value models that can capture both key forms of extremal dependence. We devise tools for validating return curve estimates, as well as representing their uncertainty, and compare a selection of curve estimation techniques through simulation studies. We apply the methodology to two met-ocean data sets, with diagnostics indicatinggenerally good performance.

KW - dependence modelling

KW - extremes

KW - risk measure

U2 - 10.1002/env.2797

DO - 10.1002/env.2797

M3 - Journal article

VL - 34

JO - Environmetrics

JF - Environmetrics

SN - 1180-4009

IS - 5

M1 - e2797

ER -