Modeling extreme values of processes observed at irregular time steps: Application to significant wave height
This work is motivated by the analysis of the extremal behavior of buoy and satellite data describing wave conditions in the North Atlantic Ocean. The available data sets consist of time series of significant wave height (Hs) with irregular time sampling. In such a situation, the usual statistical m...
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ftdatacite:10.48550/arxiv.1405.0807 2023-05-15T17:30:33+02:00 Modeling extreme values of processes observed at irregular time steps: Application to significant wave height Raillard, Nicolas Ailliot, Pierre Yao, Jianfeng 2014 https://dx.doi.org/10.48550/arxiv.1405.0807 https://arxiv.org/abs/1405.0807 unknown arXiv https://dx.doi.org/10.1214/13-aoas711 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Applications stat.AP FOS Computer and information sciences article-journal Article ScholarlyArticle Text 2014 ftdatacite https://doi.org/10.48550/arxiv.1405.0807 https://doi.org/10.1214/13-aoas711 2022-04-01T13:00:11Z This work is motivated by the analysis of the extremal behavior of buoy and satellite data describing wave conditions in the North Atlantic Ocean. The available data sets consist of time series of significant wave height (Hs) with irregular time sampling. In such a situation, the usual statistical methods for analyzing extreme values cannot be used directly. The method proposed in this paper is an extension of the peaks over threshold (POT) method, where the distribution of a process above a high threshold is approximated by a max-stable process whose parameters are estimated by maximizing a composite likelihood function. The efficiency of the proposed method is assessed on an extensive set of simulated data. It is shown, in particular, that the method is able to describe the extremal behavior of several common time series models with regular or irregular time sampling. The method is then used to analyze Hs data in the North Atlantic Ocean. The results indicate that it is possible to derive realistic estimates of the extremal properties of Hs from satellite data, despite its complex space--time sampling. : Published in at http://dx.doi.org/10.1214/13-AOAS711 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org) Text North Atlantic DataCite Metadata Store (German National Library of Science and Technology) |
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Applications stat.AP FOS Computer and information sciences |
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Applications stat.AP FOS Computer and information sciences Raillard, Nicolas Ailliot, Pierre Yao, Jianfeng Modeling extreme values of processes observed at irregular time steps: Application to significant wave height |
topic_facet |
Applications stat.AP FOS Computer and information sciences |
description |
This work is motivated by the analysis of the extremal behavior of buoy and satellite data describing wave conditions in the North Atlantic Ocean. The available data sets consist of time series of significant wave height (Hs) with irregular time sampling. In such a situation, the usual statistical methods for analyzing extreme values cannot be used directly. The method proposed in this paper is an extension of the peaks over threshold (POT) method, where the distribution of a process above a high threshold is approximated by a max-stable process whose parameters are estimated by maximizing a composite likelihood function. The efficiency of the proposed method is assessed on an extensive set of simulated data. It is shown, in particular, that the method is able to describe the extremal behavior of several common time series models with regular or irregular time sampling. The method is then used to analyze Hs data in the North Atlantic Ocean. The results indicate that it is possible to derive realistic estimates of the extremal properties of Hs from satellite data, despite its complex space--time sampling. : Published in at http://dx.doi.org/10.1214/13-AOAS711 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org) |
format |
Text |
author |
Raillard, Nicolas Ailliot, Pierre Yao, Jianfeng |
author_facet |
Raillard, Nicolas Ailliot, Pierre Yao, Jianfeng |
author_sort |
Raillard, Nicolas |
title |
Modeling extreme values of processes observed at irregular time steps: Application to significant wave height |
title_short |
Modeling extreme values of processes observed at irregular time steps: Application to significant wave height |
title_full |
Modeling extreme values of processes observed at irregular time steps: Application to significant wave height |
title_fullStr |
Modeling extreme values of processes observed at irregular time steps: Application to significant wave height |
title_full_unstemmed |
Modeling extreme values of processes observed at irregular time steps: Application to significant wave height |
title_sort |
modeling extreme values of processes observed at irregular time steps: application to significant wave height |
publisher |
arXiv |
publishDate |
2014 |
url |
https://dx.doi.org/10.48550/arxiv.1405.0807 https://arxiv.org/abs/1405.0807 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
https://dx.doi.org/10.1214/13-aoas711 |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1405.0807 https://doi.org/10.1214/13-aoas711 |
_version_ |
1766127395674259456 |