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On robust partial least square (pls) methods

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorTorrubias, J.A.G.
dc.contributor.authorRomera, Rosario
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned2009-12-23T10:08:06Z
dc.date.available2009-12-23T10:08:06Z
dc.date.issued1997-09
dc.description.abstractPLS regression methods have been used in applied fields for two decades. Techniques based on iteratively reweighted regression have appeared in the specialized Iiterature with the contaminated data case. We propose a new robust PLS technique based on the Stahel-Donoho estimator. Computational results showing the better robustness and efficiency of the new method are included.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10016/6215
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working papers Statistics and Econometrics
dc.relation.ispartofseries97-62-23
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.otherPartial least squares
dc.subject.otherrobust regression methods
dc.subject.otherrobust covariance matrices
dc.subject.otherStahel-Donoho estimator
dc.titleOn robust partial least square (pls) methods
dc.typeworking paper*
dspace.entity.typePublication
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