Home > Research > Publications & Outputs > A semi-parametric model for multivariate extrem...
View graph of relations

A semi-parametric model for multivariate extreme values.

Research output: Contribution to journalJournal article

Published

Standard

A semi-parametric model for multivariate extreme values. / Dixon, M. J.; Tawn, J. A.

In: Statistics and Computing, Vol. 5, No. 3, 09.1995, p. 215-252.

Research output: Contribution to journalJournal article

Harvard

Dixon, MJ & Tawn, JA 1995, 'A semi-parametric model for multivariate extreme values.', Statistics and Computing, vol. 5, no. 3, pp. 215-252. https://doi.org/10.1007/BF00142663

APA

Vancouver

Author

Dixon, M. J. ; Tawn, J. A. / A semi-parametric model for multivariate extreme values. In: Statistics and Computing. 1995 ; Vol. 5, No. 3. pp. 215-252.

Bibtex

@article{4fca76d0803348db85a3652ade71fb7e,
title = "A semi-parametric model for multivariate extreme values.",
abstract = "Threshold methods for multivariate extreme values are based on the use of asymptotically justified approximations of both the marginal distributions and the dependence structure in the joint tail. Models derived from these approximations are fitted to a region of the observed joint tail which is determined by suitably chosen high thresholds. A drawback of the existing methods is the necessity for the same thresholds to be taken for the convergence of both marginal and dependence aspects, which can result in inefficient estimation. In this paper an extension of the existing models, which removes this constraint, is proposed. The resulting model is semi-parametric and requires computationally intensive techniques for likelihood evaluation. The methods are illustrated using a coastal engineering application.",
keywords = "Extreme value theory - iterative proportional fitting - maximum likelihood - multivariate extreme value distribution - threshold methods",
author = "Dixon, {M. J.} and Tawn, {J. A.}",
year = "1995",
month = sep
doi = "10.1007/BF00142663",
language = "English",
volume = "5",
pages = "215--252",
journal = "Statistics and Computing",
issn = "0960-3174",
publisher = "Springer Netherlands",
number = "3",

}

RIS

TY - JOUR

T1 - A semi-parametric model for multivariate extreme values.

AU - Dixon, M. J.

AU - Tawn, J. A.

PY - 1995/9

Y1 - 1995/9

N2 - Threshold methods for multivariate extreme values are based on the use of asymptotically justified approximations of both the marginal distributions and the dependence structure in the joint tail. Models derived from these approximations are fitted to a region of the observed joint tail which is determined by suitably chosen high thresholds. A drawback of the existing methods is the necessity for the same thresholds to be taken for the convergence of both marginal and dependence aspects, which can result in inefficient estimation. In this paper an extension of the existing models, which removes this constraint, is proposed. The resulting model is semi-parametric and requires computationally intensive techniques for likelihood evaluation. The methods are illustrated using a coastal engineering application.

AB - Threshold methods for multivariate extreme values are based on the use of asymptotically justified approximations of both the marginal distributions and the dependence structure in the joint tail. Models derived from these approximations are fitted to a region of the observed joint tail which is determined by suitably chosen high thresholds. A drawback of the existing methods is the necessity for the same thresholds to be taken for the convergence of both marginal and dependence aspects, which can result in inefficient estimation. In this paper an extension of the existing models, which removes this constraint, is proposed. The resulting model is semi-parametric and requires computationally intensive techniques for likelihood evaluation. The methods are illustrated using a coastal engineering application.

KW - Extreme value theory - iterative proportional fitting - maximum likelihood - multivariate extreme value distribution - threshold methods

U2 - 10.1007/BF00142663

DO - 10.1007/BF00142663

M3 - Journal article

VL - 5

SP - 215

EP - 252

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 3

ER -