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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ftculeuclid:oai:CULeuclid:euclid.aoas/1396966301 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 http://projecteuclid.org/euclid.aoas/1396966301 https://doi.org/10.1214/13-AOAS711 en eng The Institute of Mathematical Statistics 1932-6157 1941-7330 Copyright 2014 Institute of Mathematical Statistics Extreme values time series max-stable process composite likelihood irregular time sampling significant wave height satellite data Text 2014 ftculeuclid https://doi.org/10.1214/13-AOAS711 2018-10-06T12:45:31Z 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. Text North Atlantic Project Euclid (Cornell University Library) The Annals of Applied Statistics 8 1 |
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Open Polar |
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Project Euclid (Cornell University Library) |
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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 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. |
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 |
The Institute of Mathematical Statistics |
publishDate |
2014 |
url |
http://projecteuclid.org/euclid.aoas/1396966301 https://doi.org/10.1214/13-AOAS711 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
1932-6157 1941-7330 |
op_rights |
Copyright 2014 Institute of Mathematical Statistics |
op_doi |
https://doi.org/10.1214/13-AOAS711 |
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The Annals of Applied Statistics |
container_volume |
8 |
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1 |
_version_ |
1766126721695744000 |