A multiple covariance approach to PLS regression with several predictor groups: Structural Equation Exploratory Regression
Methodology
2008-02-12 v2 Statistics Theory
Statistics Theory
Abstract
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.
Cite
@article{arxiv.0802.0793,
title = {A multiple covariance approach to PLS regression with several predictor groups: Structural Equation Exploratory Regression},
author = {Xavier Bry and Thomas Verron and Pierre Cazes},
journal= {arXiv preprint arXiv:0802.0793},
year = {2008}
}