Home > Research > Publications & Outputs > The multivariate Gaussian tail model: an applic...
View graph of relations

The multivariate Gaussian tail model: an application to oceanographic data.

Research output: Contribution to journalJournal article

Published

Standard

The multivariate Gaussian tail model: an application to oceanographic data. / Bortot, P.; Coles, S. G.; Tawn, Jonathan A.

In: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 49, No. 1, 2000, p. 31-49.

Research output: Contribution to journalJournal article

Harvard

Bortot, P, Coles, SG & Tawn, JA 2000, 'The multivariate Gaussian tail model: an application to oceanographic data.', Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 49, no. 1, pp. 31-49. https://doi.org/10.1111/1467-9876.00177

APA

Bortot, P., Coles, S. G., & Tawn, J. A. (2000). The multivariate Gaussian tail model: an application to oceanographic data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 49(1), 31-49. https://doi.org/10.1111/1467-9876.00177

Vancouver

Bortot P, Coles SG, Tawn JA. The multivariate Gaussian tail model: an application to oceanographic data. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2000;49(1):31-49. https://doi.org/10.1111/1467-9876.00177

Author

Bortot, P. ; Coles, S. G. ; Tawn, Jonathan A. / The multivariate Gaussian tail model: an application to oceanographic data. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 2000 ; Vol. 49, No. 1. pp. 31-49.

Bibtex

@article{72d632f71d7d4831bdbdbc2f9e7f822d,
title = "The multivariate Gaussian tail model: an application to oceanographic data.",
abstract = "Optimal design of sea-walls requires the extreme value analysis of a variety of oceanographic data. Asymptotic arguments suggest the use of multivariate extreme value models, but empirical studies based on data from several UK locations have revealed an inadequacy of this class for modelling the types of dependence that are often encountered in such data. This paper develops a specific model based on the marginal transformation of the tail of a multivariate Gaussian distribution and examines its utility in overcoming the limitations that are encountered with the current methodology. Diagnostics for the model are developed and the robustness of the model is demonstrated through a simulation study. Our analysis focuses on extreme sea-levels at Newlyn, a port in south-west England, for which previous studies had given conflicting estimates of the probability of flooding. The novel diagnostics suggest that this discrepancy may be due to the weak dependence at extreme levels between wave periods and both wave heights and still water levels. The multivariate Gaussian tail model is shown to resolve the conflict and to offer a convincing description of the extremal sea-state process at Newlyn.",
keywords = "Asymptotic independence • Extreme value theory • Gaussian distribution • Joint probabilities method • Multivariate extreme value distribution • Oceanography • Structure variable method • Threshold models",
author = "P. Bortot and Coles, {S. G.} and Tawn, {Jonathan A.}",
year = "2000",
doi = "10.1111/1467-9876.00177",
language = "English",
volume = "49",
pages = "31--49",
journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "1",

}

RIS

TY - JOUR

T1 - The multivariate Gaussian tail model: an application to oceanographic data.

AU - Bortot, P.

AU - Coles, S. G.

AU - Tawn, Jonathan A.

PY - 2000

Y1 - 2000

N2 - Optimal design of sea-walls requires the extreme value analysis of a variety of oceanographic data. Asymptotic arguments suggest the use of multivariate extreme value models, but empirical studies based on data from several UK locations have revealed an inadequacy of this class for modelling the types of dependence that are often encountered in such data. This paper develops a specific model based on the marginal transformation of the tail of a multivariate Gaussian distribution and examines its utility in overcoming the limitations that are encountered with the current methodology. Diagnostics for the model are developed and the robustness of the model is demonstrated through a simulation study. Our analysis focuses on extreme sea-levels at Newlyn, a port in south-west England, for which previous studies had given conflicting estimates of the probability of flooding. The novel diagnostics suggest that this discrepancy may be due to the weak dependence at extreme levels between wave periods and both wave heights and still water levels. The multivariate Gaussian tail model is shown to resolve the conflict and to offer a convincing description of the extremal sea-state process at Newlyn.

AB - Optimal design of sea-walls requires the extreme value analysis of a variety of oceanographic data. Asymptotic arguments suggest the use of multivariate extreme value models, but empirical studies based on data from several UK locations have revealed an inadequacy of this class for modelling the types of dependence that are often encountered in such data. This paper develops a specific model based on the marginal transformation of the tail of a multivariate Gaussian distribution and examines its utility in overcoming the limitations that are encountered with the current methodology. Diagnostics for the model are developed and the robustness of the model is demonstrated through a simulation study. Our analysis focuses on extreme sea-levels at Newlyn, a port in south-west England, for which previous studies had given conflicting estimates of the probability of flooding. The novel diagnostics suggest that this discrepancy may be due to the weak dependence at extreme levels between wave periods and both wave heights and still water levels. The multivariate Gaussian tail model is shown to resolve the conflict and to offer a convincing description of the extremal sea-state process at Newlyn.

KW - Asymptotic independence • Extreme value theory • Gaussian distribution • Joint probabilities method • Multivariate extreme value distribution • Oceanography • Structure variable method • Threshold models

U2 - 10.1111/1467-9876.00177

DO - 10.1111/1467-9876.00177

M3 - Journal article

VL - 49

SP - 31

EP - 49

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

IS - 1

ER -