An evaluation of the PoLiSh smoothed regression and the Monte Carlo Cross-Validation for the determination of the complexity of a PLS model
An evaluation of the PoLiSh smoothed regression and the Monte Carlo Cross-Validation for the determination of the complexity of a PLS model
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.
Adjusted Wold's R criterion, Complexity, Durbin-Watson criterion, Monte Carlo Cross-Validation, PLS, Smoothing
41-51
Gourvénec, S.
6ff91ad8-1a91-42fe-a3f4-1b5d6f5ce0b8
Fernández Pierna, J. A.
5207a1d4-21e7-4a25-a53b-3d06882fb901
Massart, D. L.
e45e44fe-05dd-4936-89f0-85fe53999ce5
Rutledge, D. N.
69ec038b-647d-43ec-bc38-bf9b93e43190
28 October 2003
Gourvénec, S.
6ff91ad8-1a91-42fe-a3f4-1b5d6f5ce0b8
Fernández Pierna, J. A.
5207a1d4-21e7-4a25-a53b-3d06882fb901
Massart, D. L.
e45e44fe-05dd-4936-89f0-85fe53999ce5
Rutledge, D. N.
69ec038b-647d-43ec-bc38-bf9b93e43190
Gourvénec, S., Fernández Pierna, J. A., Massart, D. L. and Rutledge, D. N.
(2003)
An evaluation of the PoLiSh smoothed regression and the Monte Carlo Cross-Validation for the determination of the complexity of a PLS model.
Chemometrics and Intelligent Laboratory Systems, 68 (1-2), .
(doi:10.1016/S0169-7439(03)00086-8).
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.
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Published date: 28 October 2003
Keywords:
Adjusted Wold's R criterion, Complexity, Durbin-Watson criterion, Monte Carlo Cross-Validation, PLS, Smoothing
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Local EPrints ID: 414612
URI: http://eprints.soton.ac.uk/id/eprint/414612
ISSN: 0169-7439
PURE UUID: acbc7433-8b49-4b4d-9076-9da36ccabc8e
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Date deposited: 05 Oct 2017 16:30
Last modified: 16 Mar 2024 04:31
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Author:
J. A. Fernández Pierna
Author:
D. L. Massart
Author:
D. N. Rutledge
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