Publication:
Non-parametric methods for circular-circular and circular-linear

dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorCarnicero, José Antonio
dc.contributor.authorWiper, Michael Peter
dc.contributor.authorAusín Olivera, María Concepción
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned2011-04-27T15:49:43Z
dc.date.available2011-04-27T15:49:43Z
dc.date.issued2011-04
dc.description.abstractWe present a non-parametric approach for the estimation of the bivariate distribution of two circular variables and the modelling of the joint distribution of a circular and a linear variable. We combine nonparametric estimates of the marginal densities of the circular and linear components with the use of class of nonparametric copulas, known as empirical Bernstein copulas, to model the dependence structure. We derive the necessary conditions to obtain continuous distributions defined on the cylinder for the circular-linear model and on the torus for the circular-circular model. We illustrate these two approaches with two sets of real environmental data
dc.format.mimetypeapplication/pdf
dc.identifier.repecws110704
dc.identifier.urihttps://hdl.handle.net/10016/10893
dc.identifier.uxxiDT/0000000936
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries11-04
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadística
dc.subject.otherBernstein polynomials
dc.subject.otherCircular distributions
dc.subject.otherCircular-Circular data
dc.subject.otherCircular-linear data
dc.subject.otherCopulas
dc.subject.otherNon-parametric estimation
dc.titleNon-parametric methods for circular-circular and circular-linear
dc.typeworking paper*
dspace.entity.typePublication
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