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Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference

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Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. / Konzen, E.; Neves, C.; Jonathan, P.
In: Environmetrics, Vol. 32, No. 4, e2667, 30.06.2021.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Konzen, E, Neves, C & Jonathan, P 2021, 'Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference', Environmetrics, vol. 32, no. 4, e2667. https://doi.org/10.1002/env.2667

APA

Konzen, E., Neves, C., & Jonathan, P. (2021). Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics, 32(4), Article e2667. https://doi.org/10.1002/env.2667

Vancouver

Konzen E, Neves C, Jonathan P. Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics. 2021 Jun 30;32(4):e2667. Epub 2021 Jan 27. doi: 10.1002/env.2667

Author

Konzen, E. ; Neves, C. ; Jonathan, P. / Modeling nonstationary extremes of storm severity : Comparing parametric and semiparametric inference. In: Environmetrics. 2021 ; Vol. 32, No. 4.

Bibtex

@article{ad35d5d98f0942f9910f0834a44751d0,
title = "Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference",
abstract = "This article compares the modeling of nonstationary extreme events using parametric models with local parametric and semiparametric approaches also motivated by extreme value theory. Specifically, three estimators are compared based on (a) (local) semiparametric moment estimation, (b) (local) maximum likelihood estimation, and (c) spline-based maximum likelihood estimation. Inference is performed in a sequential manner, highlighting the synergies between the different approaches to estimating extreme quantiles, including the T-year level and right endpoint when finite. We present a novel heuristic to estimate nonstationary extreme value threshold with exceedances varying on a circular domain, and hypothesis-testing procedures for identifying max-domain of attraction in the nonstationary setting. Bootstrapping is used to estimate nonstationary confidence bounds throughout. We provide step-by-step guides for estimation, and explore the different inference strategies in application to directional modeling of hindcast storm peak significant wave heights recorded in the North Sea. {\textcopyright} 2021 The Authors. Environmetrics published by John Wiley & Sons, Ltd.",
keywords = "circular, direction, endpoint, extreme quantile, kernel smoothing, peaks over threshold, periodic, significant wave height, spline, threshold selection",
author = "E. Konzen and C. Neves and P. Jonathan",
year = "2021",
month = jun,
day = "30",
doi = "10.1002/env.2667",
language = "English",
volume = "32",
journal = "Environmetrics",
issn = "1180-4009",
publisher = "John Wiley and Sons Ltd",
number = "4",

}

RIS

TY - JOUR

T1 - Modeling nonstationary extremes of storm severity

T2 - Comparing parametric and semiparametric inference

AU - Konzen, E.

AU - Neves, C.

AU - Jonathan, P.

PY - 2021/6/30

Y1 - 2021/6/30

N2 - This article compares the modeling of nonstationary extreme events using parametric models with local parametric and semiparametric approaches also motivated by extreme value theory. Specifically, three estimators are compared based on (a) (local) semiparametric moment estimation, (b) (local) maximum likelihood estimation, and (c) spline-based maximum likelihood estimation. Inference is performed in a sequential manner, highlighting the synergies between the different approaches to estimating extreme quantiles, including the T-year level and right endpoint when finite. We present a novel heuristic to estimate nonstationary extreme value threshold with exceedances varying on a circular domain, and hypothesis-testing procedures for identifying max-domain of attraction in the nonstationary setting. Bootstrapping is used to estimate nonstationary confidence bounds throughout. We provide step-by-step guides for estimation, and explore the different inference strategies in application to directional modeling of hindcast storm peak significant wave heights recorded in the North Sea. © 2021 The Authors. Environmetrics published by John Wiley & Sons, Ltd.

AB - This article compares the modeling of nonstationary extreme events using parametric models with local parametric and semiparametric approaches also motivated by extreme value theory. Specifically, three estimators are compared based on (a) (local) semiparametric moment estimation, (b) (local) maximum likelihood estimation, and (c) spline-based maximum likelihood estimation. Inference is performed in a sequential manner, highlighting the synergies between the different approaches to estimating extreme quantiles, including the T-year level and right endpoint when finite. We present a novel heuristic to estimate nonstationary extreme value threshold with exceedances varying on a circular domain, and hypothesis-testing procedures for identifying max-domain of attraction in the nonstationary setting. Bootstrapping is used to estimate nonstationary confidence bounds throughout. We provide step-by-step guides for estimation, and explore the different inference strategies in application to directional modeling of hindcast storm peak significant wave heights recorded in the North Sea. © 2021 The Authors. Environmetrics published by John Wiley & Sons, Ltd.

KW - circular

KW - direction

KW - endpoint

KW - extreme quantile

KW - kernel smoothing

KW - peaks over threshold

KW - periodic

KW - significant wave height

KW - spline

KW - threshold selection

U2 - 10.1002/env.2667

DO - 10.1002/env.2667

M3 - Journal article

VL - 32

JO - Environmetrics

JF - Environmetrics

SN - 1180-4009

IS - 4

M1 - e2667

ER -