Independent components techniques based on kurtosis for functional data analysis

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dc.contributor.author Peña, Daniel
dc.contributor.author Prieto, Francisco J.
dc.contributor.author Rendón, Carolina
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2014-05-12T13:59:26Z
dc.date.available 2014-05-12T13:59:26Z
dc.date.issued 2014-05
dc.identifier.uri http://hdl.handle.net/10016/18868
dc.description.abstract The 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 proposal
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 14-06
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Functional Data Analysis
dc.subject.other Functional Kurtosis
dc.subject.other Cluster Analysis
dc.subject.other Kurtosis Operator
dc.title Independent components techniques based on kurtosis for functional data analysis
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.type.version submitedVersion
dc.identifier.uxxi DT/0000001201
dc.identifier.repec ws141006
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