A procedure called GOLPE is suggested in order to detect those variables which increase the predictivity of PLS models. The procedure is based on evaluating the predictive power of a number of PLS models built by different combinations of variables selected according to a factorial design strategy. Examples are given of the efficiency of this variable selection procedure, which shows how these predictive PLS models are better than those obtained by all variables and better than the corresponding ordinary regression models

Predictive ability of regression models. Part II: Selection of the best predictive PLS model / M. Baroni; S. Clementi; G. Cruciani; G. Costantino; D. Riganelli; E. Oberrauch. - In: JOURNAL OF CHEMOMETRICS. - ISSN 0886-9383. - 6(1992), pp. 347-356. [10.1002/cem.1180060605]

Predictive ability of regression models. Part II: Selection of the best predictive PLS model

COSTANTINO, Gabriele;
1992

Abstract

A procedure called GOLPE is suggested in order to detect those variables which increase the predictivity of PLS models. The procedure is based on evaluating the predictive power of a number of PLS models built by different combinations of variables selected according to a factorial design strategy. Examples are given of the efficiency of this variable selection procedure, which shows how these predictive PLS models are better than those obtained by all variables and better than the corresponding ordinary regression models
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2437148
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