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Modelling the clustering of extreme events for short-term risk assessment

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

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Modelling the clustering of extreme events for short-term risk assessment. / Towe, Ross; Tawn, Jonathan; Eastoe, Emma et al.
In: Journal of Agricultural, Biological, and Environmental Statistics, Vol. 25, No. 1, 31.03.2020, p. 32-53.

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

Harvard

Towe, R, Tawn, J, Eastoe, E & Lamb, R 2020, 'Modelling the clustering of extreme events for short-term risk assessment', Journal of Agricultural, Biological, and Environmental Statistics, vol. 25, no. 1, pp. 32-53. https://doi.org/10.1007/s13253-019-00376-0

APA

Towe, R., Tawn, J., Eastoe, E., & Lamb, R. (2020). Modelling the clustering of extreme events for short-term risk assessment. Journal of Agricultural, Biological, and Environmental Statistics, 25(1), 32-53. https://doi.org/10.1007/s13253-019-00376-0

Vancouver

Towe R, Tawn J, Eastoe E, Lamb R. Modelling the clustering of extreme events for short-term risk assessment. Journal of Agricultural, Biological, and Environmental Statistics. 2020 Mar 31;25(1):32-53. Epub 2019 Aug 28. doi: 10.1007/s13253-019-00376-0

Author

Towe, Ross ; Tawn, Jonathan ; Eastoe, Emma et al. / Modelling the clustering of extreme events for short-term risk assessment. In: Journal of Agricultural, Biological, and Environmental Statistics. 2020 ; Vol. 25, No. 1. pp. 32-53.

Bibtex

@article{b3ff68c298934e1187e3d6f9a3b95c39,
title = "Modelling the clustering of extreme events for short-term risk assessment",
abstract = "Reliable estimates of the occurrence rates of extreme events are highly important for insurance companies, government agencies and the general public. The rarity of an extreme event is typically expressed through its return period, i.e. the expected waiting time between events of the observed size if the extreme events of the processes are independent and identically distributed. A major limitation with this measure is when an unexpectedly high number of events occur within the next few months immediately after a T year event, with T large. Such instances undermine the trust in the quality of risk estimates. The clustering of apparently independent extreme events can occur as a result of local non-stationarity of the process, which can be explained by covariates or random effects. We show how accounting for these covariates and random effects provides more accurate estimates of return levels and aids short-term risk assessment through the use of a complementary new risk measure. Supplementary materials accompanying this paper appear online.",
keywords = "Clustering, Covariate modelling, Extreme events, Flood risk assessment, Local non-stationarity, Random effects",
author = "Ross Towe and Jonathan Tawn and Emma Eastoe and Rob Lamb",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/s13253-019-00376-0",
year = "2020",
month = mar,
day = "31",
doi = "10.1007/s13253-019-00376-0",
language = "English",
volume = "25",
pages = "32--53",
journal = "Journal of Agricultural, Biological, and Environmental Statistics",
publisher = "Springer New York LLC",
number = "1",

}

RIS

TY - JOUR

T1 - Modelling the clustering of extreme events for short-term risk assessment

AU - Towe, Ross

AU - Tawn, Jonathan

AU - Eastoe, Emma

AU - Lamb, Rob

N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s13253-019-00376-0

PY - 2020/3/31

Y1 - 2020/3/31

N2 - Reliable estimates of the occurrence rates of extreme events are highly important for insurance companies, government agencies and the general public. The rarity of an extreme event is typically expressed through its return period, i.e. the expected waiting time between events of the observed size if the extreme events of the processes are independent and identically distributed. A major limitation with this measure is when an unexpectedly high number of events occur within the next few months immediately after a T year event, with T large. Such instances undermine the trust in the quality of risk estimates. The clustering of apparently independent extreme events can occur as a result of local non-stationarity of the process, which can be explained by covariates or random effects. We show how accounting for these covariates and random effects provides more accurate estimates of return levels and aids short-term risk assessment through the use of a complementary new risk measure. Supplementary materials accompanying this paper appear online.

AB - Reliable estimates of the occurrence rates of extreme events are highly important for insurance companies, government agencies and the general public. The rarity of an extreme event is typically expressed through its return period, i.e. the expected waiting time between events of the observed size if the extreme events of the processes are independent and identically distributed. A major limitation with this measure is when an unexpectedly high number of events occur within the next few months immediately after a T year event, with T large. Such instances undermine the trust in the quality of risk estimates. The clustering of apparently independent extreme events can occur as a result of local non-stationarity of the process, which can be explained by covariates or random effects. We show how accounting for these covariates and random effects provides more accurate estimates of return levels and aids short-term risk assessment through the use of a complementary new risk measure. Supplementary materials accompanying this paper appear online.

KW - Clustering

KW - Covariate modelling

KW - Extreme events

KW - Flood risk assessment

KW - Local non-stationarity

KW - Random effects

U2 - 10.1007/s13253-019-00376-0

DO - 10.1007/s13253-019-00376-0

M3 - Journal article

VL - 25

SP - 32

EP - 53

JO - Journal of Agricultural, Biological, and Environmental Statistics

JF - Journal of Agricultural, Biological, and Environmental Statistics

IS - 1

ER -