La régression PLS: Théorie et pratique
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De nombreux probl�mes industriels ou de management peuvent �tre d�crits sous la forme d'un syst�me � entr�es-sorties: les variables de sortie Y du syst�me d�pendent de variables d'entr�e X plus ou moins contr�lables. Il s'agit de comprendre et de d�crire les relations souvent tr�s complexes entre X et Y, en l'absence d'un mod�le th�orique. De nombreux probl�mes industriels ou de management peuvent �tre d�crits sous la forme d'un syst�me � entr�es-sorties: les variables de sortie Y du syst�me d�pendent de variables d'entr�e X plus ou moins contr�lables. Il s'agit de comprendre et de d�crire les relations souvent tr�s complexes entre X et Y, en l'absence d'un mod�le th�orique. La r�gression PLS (Partial Least Squares) est une m�thode d'analyse des donn�es sp�cifiquement construite pour l'�tude de ce type de probl�me. Elle a �t� propos�e en 1983 par Svante Wold et ses collaborateurs et conna�t depuis de grands d�veloppements, principalement dans le domaine des industries chimiques, p�troli�res et agro-alimentaires. La r�gression PLS doit pouvoir s'appliquer � de nombreux domaines avec le m�me succ�s qu'en chimie. C'est ce que nous voulons d�montrer dans ce livre dont l'objet est de faire le point sur cette m�thode, � la fois sur les plans th�orique et pratique. Sur le plan th�orique, nous avons eu trois objectifs: Situer la r�gression PLS parmi les m�thodes d'association et de pr�diction en analyse des donn�es: analyse canonique, analyse factorielle inter-batteries, analyse des redondances, algorithme NIPALS, algorithme SIMPLS et approche PLS. D�crire l'algorithme de r�gression PLS dans sa forme originale telle qu'elle est programm�e dans des logiciels comme SIMCA ou The Unscrambler. Pr�senter en d�tail les principales propri�t�s math�matiques de la r�gression PLS car leur connaissance est essentielle pour une bonne utilisation de la m�thode. Sur le plan pratique, nous illustrons l'apport de la r�gression PLS en l'utilisant sur de nombreux exemples tir�s de
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