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dc.contributor.authorBry, Xavier
dc.contributor.authorVerron, Thomas
dc.contributor.authorCazes, Pierre
dc.date.accessioned2010-03-22T13:54:16Z
dc.date.available2010-03-22T13:54:16Z
dc.date.issued2008
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/3757
dc.language.isoenen
dc.subjectLinear Regressionen
dc.subjectLatent Variablesen
dc.subjectPLS Path Modellingen
dc.subjectPLS Regressionen
dc.subjectStructural Equation Modelsen
dc.subjectSEERen
dc.subject.ddc519en
dc.titleA multiple covariance approach to PLS regression with several predictor groups: Structural Equation Exploratory Regressionen
dc.typeDocument de travail / Working paper
dc.description.abstractenA variable group Y is assumed to depend upon R thematic variable groups X 1, ..., X R . We assume that components in Y depend linearly upon components in the Xr's. In this work, we propose a multiple covariance criterion which extends that of PLS regression to this multiple predictor groups situation. On this criterion, we build a PLS-type exploratory method - Structural Equation Exploratory Regression (SEER) - that allows to simultaneously perform dimension reduction in groups and investigate the linear model of the components. SEER uses the multidimensional structure of each group. An application example is given.en
dc.publisher.nameUniversité Paris-Dauphine
dc.publisher.cityParis
dc.identifier.citationpages34en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00239491/en/en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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