GeD spline estimation of multivariate Archimedean copulas Dimitrova, Dimitrina en_US Kaishev, Vladimir en_US Penev, Spiridon en_US 2021-11-25T13:41:03Z 2021-11-25T13:41:03Z 2008 en_US
dc.description.abstract A new multivariate Archimedean copula estimation method is proposed in a non-parametric setting. The method uses the so-called Geometrically Designed splines (GeD splines) to represent the cdf of a random variable Wθ, obtained through the probability integral transform of an Archimedean copula with parameter θ. Sufficient conditions for the GeD spline estimator to possess the properties of the underlying theoretical cdf, K(θ,t), of Wθ, are given. The latter conditions allow for defining a three-step estimation procedure for solving the resulting non-linear regression problem with linear inequality constraints. In the proposed procedure, finding the number and location of the knots and the coefficients of the unconstrained GeD spline estimator and solving the constraint least-squares optimisation problem are separated. Thus, the resulting spline estimator View the MathML source is used to recover the generator and the related Archimedean copula by solving an ordinary differential equation. The proposed method is truly multivariate, it brings about numerical efficiency and as a result can be applied with large volumes of data and for dimensions d≥2, as illustrated by the numerical examples presented. en_US
dc.identifier.issn 0167-9473 en_US
dc.language English
dc.language.iso EN en_US
dc.rights CC BY-NC-ND 3.0 en_US
dc.rights.uri en_US
dc.source Legacy MARC en_US
dc.title GeD spline estimation of multivariate Archimedean copulas en_US
dc.type Journal Article en
dcterms.accessRights metadata only access
dspace.entity.type Publication en_US
unsw.identifier.doiPublisher en_US
unsw.relation.faculty Science
unsw.relation.ispartofissue 7 en_US
unsw.relation.ispartofjournal Computational Statistics & Data Analysis en_US
unsw.relation.ispartofpagefrompageto 3570-3582 en_US
unsw.relation.ispartofvolume 52 en_US
unsw.relation.originalPublicationAffiliation Dimitrova, Dimitrina en_US
unsw.relation.originalPublicationAffiliation Kaishev, Vladimir en_US
unsw.relation.originalPublicationAffiliation Penev, Spiridon, Mathematics & Statistics, Faculty of Science, UNSW en_US School of Mathematics & Statistics *
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