A multivariate pseudo-likelihood approach to estimating directional ocean wave models
File(s)
Published version
Author(s)
Grainger, Jake P
Sykulski, Adam M
Ewans, Kevin
Hansen, Hans F
Jonathan, Philip
Type
Journal Article
Abstract
Ocean buoy data in the form of high frequency multivariate time series are routinely recorded at many locations in the world's oceans. Such data can be used to characterise the ocean wavefield, which is important for numerous socio-economic and scientific reasons. This characterisation is typically achieved by modelling the frequency-direction spectrum, which decomposes spatiotemporal variability by both frequency and direction. State-of-the-art methods for estimating the parameters of such models do not make use of the full spatiotemporal content of the buoy observations due to unnecessary assumptions and smoothing steps. We explain how the multivariate debiased Whittle likelihood can be used to jointly estimate all parameters of such frequency-direction spectra directly from the recorded time series. When applied to North Sea buoy data, debiased Whittle likelihood inference reveals smooth evolution of spectral parameters over time. We discuss challenging practical issues including model misspecification, and provide guidelines for future application of the method.
Date Issued
2023-06-01
Date Acceptance
2022-12-15
Citation
Journal of the Royal Statistical Society Series C: Applied Statistics, 2023, 72 (3), pp.544-565
ISSN
0035-9254
Publisher
Oxford University Press
Start Page
544
End Page
565
Journal / Book Title
Journal of the Royal Statistical Society Series C: Applied Statistics
Volume
72
Issue
3
Copyright Statement
© (RSS) Royal Statistical Society 2023.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
http://arxiv.org/abs/2202.03773v1
Subjects
stat.AP
stat.AP
Publication Status
Published
Date Publish Online
2023-04-24