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

Bry, Xavier; Verron, Thomas; Cazes, Pierre

Bry, Xavier; Verron, Thomas; Cazes, Pierre (2008), A multiple covariance approach to PLS regression with several predictor groups: Structural Equation Exploratory Regression. https://basepub.dauphine.fr/handle/123456789/3757

Type

Document de travail / Working paper

External document link

http://hal.archives-ouvertes.fr/hal-00239491/en/

Date

2008

Publisher

Université Paris-Dauphine

Published in

Paris

Metadata

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Author(s)

Bry, Xavier
Verron, Thomas
Cazes, Pierre

Abstract (EN)

A 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.

Subjects / Keywords

Linear Regression; Latent Variables; PLS Path Modelling; PLS Regression; Structural Equation Models; SEER