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Statistical modelling of extreme ocean environments for marine design: A review

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Statistical modelling of extreme ocean environments for marine design: A review. / Jonathan, P.; Ewans, K.

In: Ocean Engineering, Vol. 62, 2013, p. 91-109.

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Jonathan P, Ewans K. Statistical modelling of extreme ocean environments for marine design: A review. Ocean Engineering. 2013;62:91-109. doi: 10.1016/j.oceaneng.2013.01.004

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Jonathan, P. ; Ewans, K. / Statistical modelling of extreme ocean environments for marine design: A review. In: Ocean Engineering. 2013 ; Vol. 62. pp. 91-109.

Bibtex

@article{50c4ef7b123d4c04b9f04d915a44913a,
title = "Statistical modelling of extreme ocean environments for marine design: A review",
abstract = "We review aspects of extreme value modelling relevant to characterisation of ocean environments and the design of marine structures, summarising basic concepts, modelling with covariates and multivariate modelling (including conditional and spatial extremes). We outline Bayesian inference for extremes and reference software resources for extreme value modelling. Extreme value analysis is inherently different to other empirical modelling, in that estimating the tail (rather than the body) of a distribution from a sample of data, and extrapolation beyond the sample (rather than interpolation within) is demanded. Intuition accumulated from other areas of empirical modelling can be misleading. Careful consideration of the effects of sample size, measurement scale, threshold selection and serial dependence, associated uncertainties and implications of choices made is essential. Incorporation of covariate effects when necessary improves inference. Suitable tools (e.g. based on additive models, splines, random fields, spatial processes) have been developed, but their use is restricted in general to academia. Effective modelling of multivariate extremes will improve the specification of design conditions for systems whose response cannot be easily characterised in terms of one variable. Approaches such as the conditional extremes model are easily implemented, and provide generalisations of existing marine design approaches (e.g. for primary and associated variables). Software is available, but again generally only for academic use. Modelling spatial dependence rigourously will provide single extreme value models applicable to spatial neighbourhoods including complete ocean basins, avoiding the need for procedures such as site pooling. Indeed, once the model is established, the metocean engineer may not ever need to perform further extreme value analysis for that basin in principle. Spatial extremes is an area of active research in the statistics community. A limited number of appropriate models have been deployed (e.g. for precipitation, temperature and metocean applications). Software is available, but again for specialist use. Bayesian inference provides a consistent framework for inference and is rapidly becoming the standard approach in academia. It appears inevitable that, in time, Bayesian inference will also be regarded as the standard in ocean engineering applications. Implementation of Bayesian methods requires some expertise. Software is available, but again generally only used by statistical specialists. {\textcopyright} 2013 Elsevier Ltd.",
keywords = "Bayesian, Covariate, Extreme, Multivariate, Return value, Review, Covariates, Bayesian networks, Design, Inference engines, Ocean engineering, Offshore structures, Reviews, Value engineering, Uncertainty analysis, Bayesian analysis, conceptual framework, covariance analysis, design, extreme event, multivariate analysis, oceanography, software, statistical analysis, structural analysis",
author = "P. Jonathan and K. Ewans",
year = "2013",
doi = "10.1016/j.oceaneng.2013.01.004",
language = "English",
volume = "62",
pages = "91--109",
journal = "Ocean Engineering",
issn = "0029-8018",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - Statistical modelling of extreme ocean environments for marine design: A review

AU - Jonathan, P.

AU - Ewans, K.

PY - 2013

Y1 - 2013

N2 - We review aspects of extreme value modelling relevant to characterisation of ocean environments and the design of marine structures, summarising basic concepts, modelling with covariates and multivariate modelling (including conditional and spatial extremes). We outline Bayesian inference for extremes and reference software resources for extreme value modelling. Extreme value analysis is inherently different to other empirical modelling, in that estimating the tail (rather than the body) of a distribution from a sample of data, and extrapolation beyond the sample (rather than interpolation within) is demanded. Intuition accumulated from other areas of empirical modelling can be misleading. Careful consideration of the effects of sample size, measurement scale, threshold selection and serial dependence, associated uncertainties and implications of choices made is essential. Incorporation of covariate effects when necessary improves inference. Suitable tools (e.g. based on additive models, splines, random fields, spatial processes) have been developed, but their use is restricted in general to academia. Effective modelling of multivariate extremes will improve the specification of design conditions for systems whose response cannot be easily characterised in terms of one variable. Approaches such as the conditional extremes model are easily implemented, and provide generalisations of existing marine design approaches (e.g. for primary and associated variables). Software is available, but again generally only for academic use. Modelling spatial dependence rigourously will provide single extreme value models applicable to spatial neighbourhoods including complete ocean basins, avoiding the need for procedures such as site pooling. Indeed, once the model is established, the metocean engineer may not ever need to perform further extreme value analysis for that basin in principle. Spatial extremes is an area of active research in the statistics community. A limited number of appropriate models have been deployed (e.g. for precipitation, temperature and metocean applications). Software is available, but again for specialist use. Bayesian inference provides a consistent framework for inference and is rapidly becoming the standard approach in academia. It appears inevitable that, in time, Bayesian inference will also be regarded as the standard in ocean engineering applications. Implementation of Bayesian methods requires some expertise. Software is available, but again generally only used by statistical specialists. © 2013 Elsevier Ltd.

AB - We review aspects of extreme value modelling relevant to characterisation of ocean environments and the design of marine structures, summarising basic concepts, modelling with covariates and multivariate modelling (including conditional and spatial extremes). We outline Bayesian inference for extremes and reference software resources for extreme value modelling. Extreme value analysis is inherently different to other empirical modelling, in that estimating the tail (rather than the body) of a distribution from a sample of data, and extrapolation beyond the sample (rather than interpolation within) is demanded. Intuition accumulated from other areas of empirical modelling can be misleading. Careful consideration of the effects of sample size, measurement scale, threshold selection and serial dependence, associated uncertainties and implications of choices made is essential. Incorporation of covariate effects when necessary improves inference. Suitable tools (e.g. based on additive models, splines, random fields, spatial processes) have been developed, but their use is restricted in general to academia. Effective modelling of multivariate extremes will improve the specification of design conditions for systems whose response cannot be easily characterised in terms of one variable. Approaches such as the conditional extremes model are easily implemented, and provide generalisations of existing marine design approaches (e.g. for primary and associated variables). Software is available, but again generally only for academic use. Modelling spatial dependence rigourously will provide single extreme value models applicable to spatial neighbourhoods including complete ocean basins, avoiding the need for procedures such as site pooling. Indeed, once the model is established, the metocean engineer may not ever need to perform further extreme value analysis for that basin in principle. Spatial extremes is an area of active research in the statistics community. A limited number of appropriate models have been deployed (e.g. for precipitation, temperature and metocean applications). Software is available, but again for specialist use. Bayesian inference provides a consistent framework for inference and is rapidly becoming the standard approach in academia. It appears inevitable that, in time, Bayesian inference will also be regarded as the standard in ocean engineering applications. Implementation of Bayesian methods requires some expertise. Software is available, but again generally only used by statistical specialists. © 2013 Elsevier Ltd.

KW - Bayesian

KW - Covariate

KW - Extreme

KW - Multivariate

KW - Return value

KW - Review

KW - Covariates

KW - Bayesian networks

KW - Design

KW - Inference engines

KW - Ocean engineering

KW - Offshore structures

KW - Reviews

KW - Value engineering

KW - Uncertainty analysis

KW - Bayesian analysis

KW - conceptual framework

KW - covariance analysis

KW - design

KW - extreme event

KW - multivariate analysis

KW - oceanography

KW - software

KW - statistical analysis

KW - structural analysis

U2 - 10.1016/j.oceaneng.2013.01.004

DO - 10.1016/j.oceaneng.2013.01.004

M3 - Journal article

VL - 62

SP - 91

EP - 109

JO - Ocean Engineering

JF - Ocean Engineering

SN - 0029-8018

ER -