Probabilistic description of metocean parameters by means of kernel density models 1. Theoretical background and first results

Athanassoulis, GA; Belibassakis, KA

dc.contributor.author	Athanassoulis, GA	en
dc.contributor.author	Belibassakis, KA	en
dc.date.accessioned	2014-03-01T01:18:15Z
dc.date.available	2014-03-01T01:18:15Z
dc.date.issued	2002	en
dc.identifier.issn	0141-1187	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14891
dc.subject	Kernel density model	en
dc.subject	Metocean parameters	en
dc.subject	Multivariate probability distribution	en
dc.subject.classification	Engineering, Ocean	en
dc.subject.classification	Oceanography	en
dc.subject.other	Bandwidth	en
dc.subject.other	Costs	en
dc.subject.other	Estimation	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Probability density function	en
dc.subject.other	Metocean parameters	en
dc.subject.other	Probability distributions	en
dc.subject.other	multivariate analysis	en
dc.subject.other	numerical method	en
dc.subject.other	oceanography	en
dc.title	Probabilistic description of metocean parameters by means of kernel density models 1. Theoretical background and first results	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0141-1187(02)00009-3	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0141-1187(02)00009-3	en
heal.language	English	en
heal.publicationDate	2002	en
heal.abstract	In the present work, a general kernel density model (KDM) has been introduced and assessed for the analytic representation of any empirical distribution function (univariate or multivariate) of metocean parameters. This model is based on the concept of kernel density function, which has been first introduced in the context of non-parametric discriminant analysis by Fix and Hodges [Nonparametric discrimination: consistency properties, 195 11, and generalised to the multivariate case by Cacoulos [Ann Inst Math 18 (1966) 178]. In its standard form, the kernel density estimators are applied to given samples of observations, producing analytical (yet non-parametric) estimations of the unknown (underlying) probability density functions. Motivated by the fact that, in many practical applications, metocean data are available only in the form of histograms (grouped data), the present KDM is implemented in a way permitting to obtain analytical estimates of the underlying probability distributions on the basis of grouped data. The main features of the proposed KDM are: (i) it can treat multivariate data with very reasonable computational cost, (ii) it is flexible enough to represent quite general distribution patterns both in the univariate and in the multivariate case, (iii) with an appropriate selection of the bandwidth, results in very satisfactory representations, avoiding local pathologies, (iv) it is marginally consistent, i.e. any marginal distribution calculated by integration front the multivariate model is identical with the corresponding marginal KDM, obtained directly from the marginal data, First numerical results are presented herein, showing that the performance of the present model is very satisfactory for all the wave parameters studied, univariate, bivariate, the trivariate (H-s, T-m, Theta(m)), and conditionals. A more detailed investigation, also including applications to other metocean parameters, will be presented as a second pail. (C) 2002 Elsevier Science Ltd All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Applied Ocean Research	en
dc.identifier.doi	10.1016/S0141-1187(02)00009-3	en
dc.identifier.isi	ISI:000177856800001	en
dc.identifier.volume	24	en
dc.identifier.issue	1	en
dc.identifier.spage	1	en
dc.identifier.epage	20	en