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Extreme value predictions based on nonstationary time series of wave data
10.1002/env.742.abs
A new method for calculating return periods of various level values from nonstationary time series data is presented. The key idea of the method is a new definition of the return period, based on the MEan Number of Upcrossings of the level x* (MENU method). In the present article, the case of Gaussian periodically correlated time series is studied in detail. The whole procedure is numerically implemented and applied to synthetic wave data in order to test the stability of the method. Results obtained by using several variants of traditional methods (Gumbel's approach and the POT method) are also presented for comparison purposes. The results of the MENU method showed an extraordinary stability, in contrast to the wide variability of the traditional methods. The predictions obtained by means of the MENU method are lower than the traditional predictions. This is in accordance with the results of other methods that also take into account the dependence structure of the examined time series. Copyright © 2005 John Wiley & Sons, Ltd.
Extreme value predictions based on nonstationary time series of wave data
10.1002/env.742.abs
A new method for calculating return periods of various level values from nonstationary time series data is presented. The key idea of the method is a new definition of the return period, based on the MEan Number of Upcrossings of the level x* (MENU method). In the present article, the case of Gaussian periodically correlated time series is studied in detail. The whole procedure is numerically implemented and applied to synthetic wave data in order to test the stability of the method. Results obtained by using several variants of traditional methods (Gumbel's approach and the POT method) are also presented for comparison purposes. The results of the MENU method showed an extraordinary stability, in contrast to the wide variability of the traditional methods. The predictions obtained by means of the MENU method are lower than the traditional predictions. This is in accordance with the results of other methods that also take into account the dependence structure of the examined time series. Copyright © 2005 John Wiley & Sons, Ltd.
Extreme value predictions based on nonstationary time series of wave data
Stefanakos, Christos N. (author) / Athanassoulis, Gerassimos A. (author)
Environmetrics ; 17 ; 25-46
2006-02-01
22 pages
Article (Journal)
Electronic Resource
English
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