A multiple covariance approach to PLS regression with several predictor groups: Structural Equation Exploratory Regression

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dc.creatorBry, Xavier
dc.creatorVerron, Thomas
dc.creatorCazes, Pierre
dc.date2008-02-06
dc.date2008-02-11
dc.date.accessioned2026-07-07T09:19:45Z
dc.date.available2026-07-07T09:19:45Z
dc.descriptionA 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.
dc.identifierhttps://arxiv.org/abs/0802.0793
dc.identifierhttp://arxiv.org/abs/0802.0793
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/154505
dc.subjectMethodology
dc.subjectStatistics Theory
dc.titleA multiple covariance approach to PLS regression with several predictor groups: Structural Equation Exploratory Regression
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