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Temporal evolution of the extreme excursions of multivariate k $$ k $$ th order Markov processes with application to oceanographic data

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Temporal evolution of the extreme excursions of multivariate k $$ k $$ th order Markov processes with application to oceanographic data. / Tendijck, Stan; Tawn, Jonathan; Randell, David et al.
In: Environmetrics, 03.12.2023.

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

Harvard

Tendijck, S, Tawn, J, Randell, D & Jonathan, P 2023, 'Temporal evolution of the extreme excursions of multivariate k $$ k $$ th order Markov processes with application to oceanographic data', Environmetrics. https://doi.org/10.1002/env.2834

APA

Tendijck, S., Tawn, J., Randell, D., & Jonathan, P. (2023). Temporal evolution of the extreme excursions of multivariate k $$ k $$ th order Markov processes with application to oceanographic data. Environmetrics. Advance online publication. https://doi.org/10.1002/env.2834

Vancouver

Tendijck S, Tawn J, Randell D, Jonathan P. Temporal evolution of the extreme excursions of multivariate k $$ k $$ th order Markov processes with application to oceanographic data. Environmetrics. 2023 Dec 3. Epub 2023 Dec 3. doi: 10.1002/env.2834

Author

Tendijck, Stan ; Tawn, Jonathan ; Randell, David et al. / Temporal evolution of the extreme excursions of multivariate k $$ k $$ th order Markov processes with application to oceanographic data. In: Environmetrics. 2023.

Bibtex

@article{4d28a03f107b433bae242a2e1a8fe51b,
title = "Temporal evolution of the extreme excursions of multivariate k $$ k $$ th order Markov processes with application to oceanographic data",
abstract = "We develop two models for the temporal evolution of extreme events of multivariate k $$ k $$ th order Markov processes. The foundation of our methodology lies in the conditional extremes model of Heffernan and Tawn (Journal of the Royal Statistical Society: Series B (Methodology), 2014, 66, 497–546), and it naturally extends the work of Winter and Tawn (Journal of the Royal Statistical Society: Series C (Applied Statistics), 2016, 65, 345–365; Extremes, 2017, 20, 393–415) and Tendijck et al. (Environmetrics 2019, 30, e2541) to include multivariate random variables. We use cross‐validation‐type techniques to develop a model order selection procedure, and we test our models on two‐dimensional meteorological‐oceanographic data with directional covariates for a location in the northern North Sea. We conclude that the newly‐developed models perform better than the widely used historical matching methodology for these data.",
author = "Stan Tendijck and Jonathan Tawn and David Randell and Philip Jonathan",
year = "2023",
month = dec,
day = "3",
doi = "10.1002/env.2834",
language = "English",
journal = "Environmetrics",
issn = "1180-4009",
publisher = "John Wiley and Sons Ltd",

}

RIS

TY - JOUR

T1 - Temporal evolution of the extreme excursions of multivariate k $$ k $$ th order Markov processes with application to oceanographic data

AU - Tendijck, Stan

AU - Tawn, Jonathan

AU - Randell, David

AU - Jonathan, Philip

PY - 2023/12/3

Y1 - 2023/12/3

N2 - We develop two models for the temporal evolution of extreme events of multivariate k $$ k $$ th order Markov processes. The foundation of our methodology lies in the conditional extremes model of Heffernan and Tawn (Journal of the Royal Statistical Society: Series B (Methodology), 2014, 66, 497–546), and it naturally extends the work of Winter and Tawn (Journal of the Royal Statistical Society: Series C (Applied Statistics), 2016, 65, 345–365; Extremes, 2017, 20, 393–415) and Tendijck et al. (Environmetrics 2019, 30, e2541) to include multivariate random variables. We use cross‐validation‐type techniques to develop a model order selection procedure, and we test our models on two‐dimensional meteorological‐oceanographic data with directional covariates for a location in the northern North Sea. We conclude that the newly‐developed models perform better than the widely used historical matching methodology for these data.

AB - We develop two models for the temporal evolution of extreme events of multivariate k $$ k $$ th order Markov processes. The foundation of our methodology lies in the conditional extremes model of Heffernan and Tawn (Journal of the Royal Statistical Society: Series B (Methodology), 2014, 66, 497–546), and it naturally extends the work of Winter and Tawn (Journal of the Royal Statistical Society: Series C (Applied Statistics), 2016, 65, 345–365; Extremes, 2017, 20, 393–415) and Tendijck et al. (Environmetrics 2019, 30, e2541) to include multivariate random variables. We use cross‐validation‐type techniques to develop a model order selection procedure, and we test our models on two‐dimensional meteorological‐oceanographic data with directional covariates for a location in the northern North Sea. We conclude that the newly‐developed models perform better than the widely used historical matching methodology for these data.

U2 - 10.1002/env.2834

DO - 10.1002/env.2834

M3 - Journal article

JO - Environmetrics

JF - Environmetrics

SN - 1180-4009

ER -