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Modelling dependence within joint tail regions

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Standard

Modelling dependence within joint tail regions. / Ledford, A. W.; Tawn, J. A.
In: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 59, No. 2, 1997, p. 475-499.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Ledford, AW & Tawn, JA 1997, 'Modelling dependence within joint tail regions', Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 59, no. 2, pp. 475-499. https://doi.org/10.1111/1467-9868.00080

APA

Ledford, A. W., & Tawn, J. A. (1997). Modelling dependence within joint tail regions. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 59(2), 475-499. https://doi.org/10.1111/1467-9868.00080

Vancouver

Ledford AW, Tawn JA. Modelling dependence within joint tail regions. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 1997;59(2):475-499. doi: 10.1111/1467-9868.00080

Author

Ledford, A. W. ; Tawn, J. A. / Modelling dependence within joint tail regions. In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 1997 ; Vol. 59, No. 2. pp. 475-499.

Bibtex

@article{0eaa737df73640e3b262d505ab31949a,
title = "Modelling dependence within joint tail regions",
abstract = "Standard approaches for modelling dependence within joint tail regions are based on extreme value methods which assume max-stability, a particular form of joint tail dependence. We develop joint tail models based on a broader class of dependence structure which provides a natural link between max-stable models and weaker forms of dependence including independence and negative association. This approach overcomes many of the problems that are encountered with standard methods and is the basis for a Poisson process representation that generalizes existing bivariate results. We apply the new techniques to simulated and environmental data, and demonstrate the marked advantage that the new approach offers for joint tail extrapolation.",
keywords = "asymptotic independence • coefficient of tail dependence • componentwise maxima • extreme value theory • maximum likelihood • non-homogeneous Poisson process • rates of convergence • slowly varying functions",
author = "Ledford, {A. W.} and Tawn, {J. A.}",
year = "1997",
doi = "10.1111/1467-9868.00080",
language = "English",
volume = "59",
pages = "475--499",
journal = "Journal of the Royal Statistical Society: Series B (Statistical Methodology)",
issn = "1369-7412",
publisher = "Wiley-Blackwell",
number = "2",

}

RIS

TY - JOUR

T1 - Modelling dependence within joint tail regions

AU - Ledford, A. W.

AU - Tawn, J. A.

PY - 1997

Y1 - 1997

N2 - Standard approaches for modelling dependence within joint tail regions are based on extreme value methods which assume max-stability, a particular form of joint tail dependence. We develop joint tail models based on a broader class of dependence structure which provides a natural link between max-stable models and weaker forms of dependence including independence and negative association. This approach overcomes many of the problems that are encountered with standard methods and is the basis for a Poisson process representation that generalizes existing bivariate results. We apply the new techniques to simulated and environmental data, and demonstrate the marked advantage that the new approach offers for joint tail extrapolation.

AB - Standard approaches for modelling dependence within joint tail regions are based on extreme value methods which assume max-stability, a particular form of joint tail dependence. We develop joint tail models based on a broader class of dependence structure which provides a natural link between max-stable models and weaker forms of dependence including independence and negative association. This approach overcomes many of the problems that are encountered with standard methods and is the basis for a Poisson process representation that generalizes existing bivariate results. We apply the new techniques to simulated and environmental data, and demonstrate the marked advantage that the new approach offers for joint tail extrapolation.

KW - asymptotic independence • coefficient of tail dependence • componentwise maxima • extreme value theory • maximum likelihood • non-homogeneous Poisson process • rates of convergence • slowly varying functions

U2 - 10.1111/1467-9868.00080

DO - 10.1111/1467-9868.00080

M3 - Journal article

VL - 59

SP - 475

EP - 499

JO - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

JF - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

SN - 1369-7412

IS - 2

ER -