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Bayesian inference for nonstationary marginal extremes

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Bayesian inference for nonstationary marginal extremes. / Randell, D.; Turnbull, K.; Ewans, K. et al.
In: Environmetrics, Vol. 27, No. 7, 11.2016, p. 439-450.

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

Harvard

Randell, D, Turnbull, K, Ewans, K & Jonathan, P 2016, 'Bayesian inference for nonstationary marginal extremes', Environmetrics, vol. 27, no. 7, pp. 439-450. https://doi.org/10.1002/env.2403

APA

Randell, D., Turnbull, K., Ewans, K., & Jonathan, P. (2016). Bayesian inference for nonstationary marginal extremes. Environmetrics, 27(7), 439-450. https://doi.org/10.1002/env.2403

Vancouver

Randell D, Turnbull K, Ewans K, Jonathan P. Bayesian inference for nonstationary marginal extremes. Environmetrics. 2016 Nov;27(7):439-450. Epub 2016 Sept 15. doi: 10.1002/env.2403

Author

Randell, D. ; Turnbull, K. ; Ewans, K. et al. / Bayesian inference for nonstationary marginal extremes. In: Environmetrics. 2016 ; Vol. 27, No. 7. pp. 439-450.

Bibtex

@article{036609a43b084a62a32ecf036a8465d1,
title = "Bayesian inference for nonstationary marginal extremes",
abstract = "We propose a simple piecewise model for a sample of peaks-over-threshold, nonstationary with respect to multidimensional covariates, and estimate it using a carefully designed and computationally efficient Bayesian inference. Model parameters are themselves parameterized as functions of covariates using penalized B-spline representations. This allows detailed characterization of non-stationarity extreme environments. The approach gives similar inferences to a comparable frequentist penalized maximum likelihood method, but is computationally considerably more efficient and allows a more complete characterization of uncertainty in a single modelling step. We use the model to quantify the joint directional and seasonal variation of storm peak significant wave height at a northern North Sea location and estimate predictive directional–seasonal return value distributions necessary for the design and reliability assessment of marine and coastal structures.",
keywords = "Bayesian, covariate, extreme, generalized Pareto, non-stationarity, ocean wave, Poisson process, return value , spline, storm severity, Weibull",
author = "D. Randell and K. Turnbull and K. Ewans and P. Jonathan",
note = "env.2403",
year = "2016",
month = nov,
doi = "10.1002/env.2403",
language = "English",
volume = "27",
pages = "439--450",
journal = "Environmetrics",
issn = "1099-095X",
publisher = "John Wiley and Sons Ltd",
number = "7",

}

RIS

TY - JOUR

T1 - Bayesian inference for nonstationary marginal extremes

AU - Randell, D.

AU - Turnbull, K.

AU - Ewans, K.

AU - Jonathan, P.

N1 - env.2403

PY - 2016/11

Y1 - 2016/11

N2 - We propose a simple piecewise model for a sample of peaks-over-threshold, nonstationary with respect to multidimensional covariates, and estimate it using a carefully designed and computationally efficient Bayesian inference. Model parameters are themselves parameterized as functions of covariates using penalized B-spline representations. This allows detailed characterization of non-stationarity extreme environments. The approach gives similar inferences to a comparable frequentist penalized maximum likelihood method, but is computationally considerably more efficient and allows a more complete characterization of uncertainty in a single modelling step. We use the model to quantify the joint directional and seasonal variation of storm peak significant wave height at a northern North Sea location and estimate predictive directional–seasonal return value distributions necessary for the design and reliability assessment of marine and coastal structures.

AB - We propose a simple piecewise model for a sample of peaks-over-threshold, nonstationary with respect to multidimensional covariates, and estimate it using a carefully designed and computationally efficient Bayesian inference. Model parameters are themselves parameterized as functions of covariates using penalized B-spline representations. This allows detailed characterization of non-stationarity extreme environments. The approach gives similar inferences to a comparable frequentist penalized maximum likelihood method, but is computationally considerably more efficient and allows a more complete characterization of uncertainty in a single modelling step. We use the model to quantify the joint directional and seasonal variation of storm peak significant wave height at a northern North Sea location and estimate predictive directional–seasonal return value distributions necessary for the design and reliability assessment of marine and coastal structures.

KW - Bayesian

KW - covariate

KW - extreme

KW - generalized Pareto

KW - non-stationarity

KW - ocean wave

KW - Poisson process

KW - return value

KW - spline

KW - storm severity

KW - Weibull

U2 - 10.1002/env.2403

DO - 10.1002/env.2403

M3 - Journal article

VL - 27

SP - 439

EP - 450

JO - Environmetrics

JF - Environmetrics

SN - 1099-095X

IS - 7

ER -