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An evaluation of the PoLiSh smoothed regression and the Monte Carlo Cross-Validation for the determination of the complexity of a PLS model


  • Gourvenec, S. , Fernández Pierna, J.A. , Massart, D. & Rutledge, D. (2003). An evaluation of the PoLiSh smoothed regression and the Monte Carlo Cross-Validation for the determination of the complexity of a PLS model. Chemom. Intell. Lab. Syst. 68: (1-2), 41-51.
Type Journal Article
Year 2003
Title An evaluation of the PoLiSh smoothed regression and the Monte Carlo Cross-Validation for the determination of the complexity of a PLS model
Journal Chemom. Intell. Lab. Syst.
Label U15-0534-fernandez-2003
Recnumber 376
Volume 68
Issue 1-2
Pages 41-51
Date 28/10/2003
Endnote Keywords PLS, complexity, Monte Carlo Cross-Validation, smoothing, Durbin-Watson criterion, adjusted Wold's R criterion|least-squares methods|spectral analyses|differentiation|calibration|
Abstract A crucial point of the PLS algorithm is the selection of the right number of factors or components (i.e., the determination of the optimal complexity of the system to avoid overfitting). The leave-one-out cross-validation is usually used to determine the optimal complexity of a PLS model, but in practice, it is found that often too many components are retained with this method. In this study, the Monte Carlo Cross-Validation (MCCV) and the PoLiSh smoothed regression are used and compared with the better known adjusted Wold's R criterion. (C) 2003 Elsevier B.V. All rights reserved.
Notes EnglishArticleCHEMOMETR INTELL LAB SYST728TA
Author address Fernandez Pierna Juan Antonio, Quality Department of Agro-food Products, Walloon Agricultural Research Centre (CRA-W), Chaussée de Namur, 24, B-5030 Gembloux, fernandez@cra.wallonie.be
Fichier U15-0534-fernandez-2003.pdf
Authors Gourvenec, S. , Fernández Pierna, J.A. , Massart, D. & Rutledge, D.