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Statistics of extreme ocean environments: Non-stationary inference for directionality and other covariate effects

Jones, Matthew; Randell, David; Ewans, Kevin; Jonathan, Philip

Statistics of extreme ocean environments: Non-stationary inference for directionality and other covariate effects Thumbnail


Authors

Matthew Jones

David Randell

Kevin Ewans

Philip Jonathan



Abstract

Numerous approaches are proposed in the literature for non-stationarity marginal extreme value inference, including different model parameterisations with respect to covariate, and different inference schemes. The objective of this paper is to compare some of these procedures critically. We generate sample realisations from generalised Pareto distributions, the parameters of which are smooth functions of a single smooth periodic covariate, specified to reflect the characteristics of actual samples from the tail of the distribution of significant wave height with direction, considered in the literature in the recent past. We estimate extreme values models (a) using Constant, Fourier, B-spline and Gaussian Process parameterisations for the functional forms of generalised Pareto shape and (adjusted) scale with respect to covariate and (b) maximum likelihood and Bayesian inference procedures. We evaluate the relative quality of inferences by estimating return value distributions for the response corresponding to a time period of 10× the (assumed) period of the original sample, and compare estimated return values distributions with the truth using Kullback–Leibler, Cramer–von Mises and Kolmogorov–Smirnov statistics. We find that Spline and Gaussian Process parameterisations, estimated by Markov chain Monte Carlo inference using the mMALA algorithm, perform equally well in terms of quality of inference and computational efficiency, and generally perform better than alternatives in those respects.

Journal Article Type Article
Acceptance Date Apr 11, 2016
Online Publication Date Apr 29, 2016
Publication Date Jun 1, 2016
Deposit Date Feb 14, 2017
Publicly Available Date Apr 29, 2017
Journal Ocean Engineering
Print ISSN 0029-8018
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 119
Pages 30-46
DOI https://doi.org/10.1016/j.oceaneng.2016.04.010
Public URL https://durham-repository.worktribe.com/output/1364531
Related Public URLs http://www.lancs.ac.uk/~jonathan/JnsRndEwnJnt15_SmtExt.pdf

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