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Independent components techniques based on kurtosis for functional data analysis

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorPeña, Daniel
dc.contributor.authorPrieto, Francisco J.
dc.contributor.authorRendón, Carolina
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadísticaes
dc.date.accessioned2014-05-12T13:59:26Z
dc.date.available2014-05-12T13:59:26Z
dc.date.issued2014-05
dc.description.abstractThe motivation for this paper arises from an article written by Peña et al. [40] in 2010,where they propose the eigenvectors associated with the extreme values of a kurtosismatrix as interesting directions to reveal the possible cluster structure of a dataset. In recent years many research papers have proposed generalizations of multivariatetechniques to the functional data case. In this paper we introduce an extension of themultivariate kurtosis for functional data, and we analyze some of its properties. Inparticular, we explore if our proposal preserves some of the properties of the kurtosisprocedures applied to the multivariate case, regarding the identification of outliers andcluster structures. This analysis is conducted considering both theoretical andexperimental properties of our proposalen
dc.format.mimetypeapplication/pdf
dc.identifier.repecws141006
dc.identifier.urihttps://hdl.handle.net/10016/18868
dc.identifier.uxxiDT/0000001201
dc.language.isoenges
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometricsen
dc.relation.ispartofseries14-06
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaEstadísticaes
dc.subject.otherFunctional Data Analysisen
dc.subject.otherFunctional Kurtosisen
dc.subject.otherCluster Analysisen
dc.subject.otherKurtosis Operatoren
dc.titleIndependent components techniques based on kurtosis for functional data analysisen
dc.typeworking paper*
dc.type.hasVersionSMUR*
dspace.entity.typePublication
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