Extreme value predictions based on nonstationary time series of wave data

Stefanakos, CN; Athanassoulis, GA

dc.contributor.author	Stefanakos, CN	en
dc.contributor.author	Athanassoulis, GA	en
dc.date.accessioned	2014-03-01T01:24:24Z
dc.date.available	2014-03-01T01:24:24Z
dc.date.issued	2006	en
dc.identifier.issn	1180-4009	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17244
dc.subject	Extreme values	en
dc.subject	Mean number of upcrossings	en
dc.subject	Nonstationary time series data	en
dc.subject	Return period	en
dc.subject	Significant wave height	en
dc.subject	Simulated data	en
dc.subject.classification	Environmental Sciences	en
dc.subject.classification	Mathematics, Interdisciplinary Applications	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	extreme event	en
dc.subject.other	return period	en
dc.subject.other	wave height	en
dc.title	Extreme value predictions based on nonstationary time series of wave data	en
heal.type	journalArticle	en
heal.identifier.primary	10.1002/env.742	en
heal.identifier.secondary	http://dx.doi.org/10.1002/env.742	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	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 (c) 2005 John Wiley & Sons, Ltd.	en
heal.publisher	JOHN WILEY & SONS LTD	en
heal.journalName	Environmetrics	en
dc.identifier.doi	10.1002/env.742	en
dc.identifier.isi	ISI:000234703200003	en
dc.identifier.volume	17	en
dc.identifier.issue	1	en
dc.identifier.spage	25	en
dc.identifier.epage	46	en