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Diagnostics for dependence within time series extremes.

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Diagnostics for dependence within time series extremes. / Tawn, Jonathan A.; Ledford, Anthony W.

In: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 65, No. 2, 05.2003, p. 521-543.

Research output: Contribution to journalJournal article

Harvard

Tawn, JA & Ledford, AW 2003, 'Diagnostics for dependence within time series extremes.', Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 65, no. 2, pp. 521-543. https://doi.org/10.1111/1467-9868.00400

APA

Tawn, J. A., & Ledford, A. W. (2003). Diagnostics for dependence within time series extremes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 65(2), 521-543. https://doi.org/10.1111/1467-9868.00400

Vancouver

Tawn JA, Ledford AW. Diagnostics for dependence within time series extremes. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2003 May;65(2):521-543. https://doi.org/10.1111/1467-9868.00400

Author

Tawn, Jonathan A. ; Ledford, Anthony W. / Diagnostics for dependence within time series extremes. In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2003 ; Vol. 65, No. 2. pp. 521-543.

Bibtex

@article{98a4d6c66c48474ca292589d637f4fe1,
title = "Diagnostics for dependence within time series extremes.",
abstract = "Summary. The analysis of extreme values within a stationary time series entails various assumptions concerning its long- and short-range dependence. We present a range of new diagnostic tools for assessing whether these assumptions are appropriate and for identifying structure within extreme events. These tools are based on tail characteristics of joint survivor functions but can be implemented by using existing estimation methods for extremes of univariate independent and identically distributed variables. Our diagnostic aids are illustrated through theoretical examples, simulation studies and by application to rainfall and exchange rate data. On the basis of these diagnostics we can explain characteristics that are found in the observed extreme events of these series and also gain insight into the properties of events that are more extreme than those observed.",
author = "Tawn, {Jonathan A.} and Ledford, {Anthony W.}",
note = "RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research",
year = "2003",
month = "5",
doi = "10.1111/1467-9868.00400",
language = "English",
volume = "65",
pages = "521--543",
journal = "Journal of the Royal Statistical Society: Series B (Statistical Methodology)",
issn = "1369-7412",
publisher = "Wiley-Blackwell",
number = "2",

}

RIS

TY - JOUR

T1 - Diagnostics for dependence within time series extremes.

AU - Tawn, Jonathan A.

AU - Ledford, Anthony W.

N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research

PY - 2003/5

Y1 - 2003/5

N2 - Summary. The analysis of extreme values within a stationary time series entails various assumptions concerning its long- and short-range dependence. We present a range of new diagnostic tools for assessing whether these assumptions are appropriate and for identifying structure within extreme events. These tools are based on tail characteristics of joint survivor functions but can be implemented by using existing estimation methods for extremes of univariate independent and identically distributed variables. Our diagnostic aids are illustrated through theoretical examples, simulation studies and by application to rainfall and exchange rate data. On the basis of these diagnostics we can explain characteristics that are found in the observed extreme events of these series and also gain insight into the properties of events that are more extreme than those observed.

AB - Summary. The analysis of extreme values within a stationary time series entails various assumptions concerning its long- and short-range dependence. We present a range of new diagnostic tools for assessing whether these assumptions are appropriate and for identifying structure within extreme events. These tools are based on tail characteristics of joint survivor functions but can be implemented by using existing estimation methods for extremes of univariate independent and identically distributed variables. Our diagnostic aids are illustrated through theoretical examples, simulation studies and by application to rainfall and exchange rate data. On the basis of these diagnostics we can explain characteristics that are found in the observed extreme events of these series and also gain insight into the properties of events that are more extreme than those observed.

U2 - 10.1111/1467-9868.00400

DO - 10.1111/1467-9868.00400

M3 - Journal article

VL - 65

SP - 521

EP - 543

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 -