Research Article

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

  1. Massimo Baroni1,
  2. Sergio Clementi1,*,
  3. Gabriele Cruciani1,
  4. Gabriele Costantino1,
  5. Daniela Riganelli1,
  6. Ermanno Oberrauch2

Article first published online: 30 MAR 2005

DOI: 10.1002/cem.1180060605

Issue

Journal of Chemometrics

Journal of Chemometrics

Volume 6, Issue 6, pages 347–356, November/December 1992

Additional Information

How to Cite

Baroni, M., Clementi, S., Cruciani, G., Costantino, G., Riganelli, D. and Oberrauch, E. (1992), Predictive ability of regression models. Part II: Selection of the best predictive PLS model. Journal of Chemometrics, 6: 347–356. doi: 10.1002/cem.1180060605

Author Information

  1. 1

    Laboratorio di Chemiometria, Dipartimento di Chimica, Università di Perugia, Via Elce di Sotto 10, I-06100 Perugia, Italy

  2. 2

    Augat Electroform SA, CH-6934 Bioggio, Switzerland

*Laboratorio di Chemiometria, Dipartimento di Chimica, Università di Perugia, Via Elce di Sotto 10, I-06100 Perugia, Italy

Publication History

  1. Issue published online: 30 MAR 2005
  2. Article first published online: 30 MAR 2005
  3. Manuscript Accepted: 31 AUG 1992
  4. Manuscript Received: 14 FEB 1992

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Keywords:

  • GOLPE;
  • PLS;
  • Regression;
  • SDEP;
  • Variable selection

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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