A penalised piecewise-linear model for non-stationary extreme value analysis of peaks over threshold. (1st January 2023)
- Record Type:
- Journal Article
- Title:
- A penalised piecewise-linear model for non-stationary extreme value analysis of peaks over threshold. (1st January 2023)
- Main Title:
- A penalised piecewise-linear model for non-stationary extreme value analysis of peaks over threshold
- Authors:
- Barlow, Anna Maria
Mackay, Ed
Eastoe, Emma
Jonathan, Philip - Abstract:
- Abstract: Metocean extremes often vary systematically with covariates such as direction and season. In this work, we present non-stationary models for the size and rate of occurrence of peaks over threshold of metocean variables with respect to one- or two-dimensional covariates. The variation of model parameters with covariate is described using a piecewise-linear function in one or two dimensions, defined with respect to pre-specified node locations on the covariate domain. Parameter roughness is regulated to provide optimal predictive performance, assessed using cross-validation, within a penalised likelihood framework for inference. Parameter uncertainty is quantified using bootstrap resampling. The models are used to estimate extremes of storm-peak significant wave height with respect to direction and season for a site in the northern North Sea. A covariate representation based on a triangulation of the direction-season domain with six nodes gives good predictive performance. The penalised piecewise-linear framework provides a flexible representation of covariate effects at reasonable computational cost. Highlights: Non-stationary model for extreme values of a variable with respect to covariates. Generalised Pareto scale and shape parameters modelled as piecewise-linear functions. Parameter variation penalised to obtain optimal predictive performance. Model is computationally efficient and sufficiently flexible to capture covariate effects. Open-source softwareAbstract: Metocean extremes often vary systematically with covariates such as direction and season. In this work, we present non-stationary models for the size and rate of occurrence of peaks over threshold of metocean variables with respect to one- or two-dimensional covariates. The variation of model parameters with covariate is described using a piecewise-linear function in one or two dimensions, defined with respect to pre-specified node locations on the covariate domain. Parameter roughness is regulated to provide optimal predictive performance, assessed using cross-validation, within a penalised likelihood framework for inference. Parameter uncertainty is quantified using bootstrap resampling. The models are used to estimate extremes of storm-peak significant wave height with respect to direction and season for a site in the northern North Sea. A covariate representation based on a triangulation of the direction-season domain with six nodes gives good predictive performance. The penalised piecewise-linear framework provides a flexible representation of covariate effects at reasonable computational cost. Highlights: Non-stationary model for extreme values of a variable with respect to covariates. Generalised Pareto scale and shape parameters modelled as piecewise-linear functions. Parameter variation penalised to obtain optimal predictive performance. Model is computationally efficient and sufficiently flexible to capture covariate effects. Open-source software available for model fitting. … (more)
- Is Part Of:
- Ocean engineering. Volume 267(2023)
- Journal:
- Ocean engineering
- Issue:
- Volume 267(2023)
- Issue Display:
- Volume 267, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 267
- Issue:
- 2023
- Issue Sort Value:
- 2023-0267-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-01-01
- Subjects:
- Extreme -- Non-stationary -- Covariate -- Penalised likelihood -- Significant wave height
Ocean engineering -- Periodicals
Ocean engineering
Periodicals
620.4162 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00298018 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.oceaneng.2022.113265 ↗
- Languages:
- English
- ISSNs:
- 0029-8018
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6231.280000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24845.xml