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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ftarchimer:oai:archimer.ifremer.fr:32888 2023-05-15T17:30:22+02:00 Modeling extreme values of processes observed at irregular time steps: Application to significant wave height Raillard, Nicolas Ailliot, Pierre Yao, Jianfeng 2014-03 application/pdf https://archimer.ifremer.fr/doc/00218/32888/31372.pdf https://doi.org/10.1214/13-AOAS711 https://archimer.ifremer.fr/doc/00218/32888/ eng eng Inst Mathematical Statistics https://archimer.ifremer.fr/doc/00218/32888/31372.pdf doi:10.1214/13-AOAS711 https://archimer.ifremer.fr/doc/00218/32888/ Institute of Mathematical Statistics, 2014 info:eu-repo/semantics/openAccess restricted use Annals Of Applied Statistics (1932-6157) (Inst Mathematical Statistics), 2014-03 , Vol. 8 , N. 1 , P. 622-647 Extreme values time series max-stable process composite likelihood irregular time sampling significant wave height satellite data text Publication info:eu-repo/semantics/article 2014 ftarchimer https://doi.org/10.1214/13-AOAS711 2021-09-23T20:25:01Z 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 maxstable 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. Article in Journal/Newspaper North Atlantic Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer) The Annals of Applied Statistics 8 1 |
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Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer) |
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ftarchimer |
language |
English |
topic |
Extreme values time series max-stable process composite likelihood irregular time sampling significant wave height satellite data |
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Extreme values time series max-stable process composite likelihood irregular time sampling significant wave height satellite data Raillard, Nicolas Ailliot, Pierre Yao, Jianfeng Modeling extreme values of processes observed at irregular time steps: Application to significant wave height |
topic_facet |
Extreme values time series max-stable process composite likelihood irregular time sampling significant wave height satellite data |
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 maxstable 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. |
format |
Article in Journal/Newspaper |
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 |
Inst Mathematical Statistics |
publishDate |
2014 |
url |
https://archimer.ifremer.fr/doc/00218/32888/31372.pdf https://doi.org/10.1214/13-AOAS711 https://archimer.ifremer.fr/doc/00218/32888/ |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Annals Of Applied Statistics (1932-6157) (Inst Mathematical Statistics), 2014-03 , Vol. 8 , N. 1 , P. 622-647 |
op_relation |
https://archimer.ifremer.fr/doc/00218/32888/31372.pdf doi:10.1214/13-AOAS711 https://archimer.ifremer.fr/doc/00218/32888/ |
op_rights |
Institute of Mathematical Statistics, 2014 info:eu-repo/semantics/openAccess restricted use |
op_doi |
https://doi.org/10.1214/13-AOAS711 |
container_title |
The Annals of Applied Statistics |
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8 |
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1 |
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1766126734905704448 |