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

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

2008-02-06

arxiv.org/abs/0802.0793v2

Statistics

Methodology

Scientific paper

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.

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