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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

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
0169-7439
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
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), 41-51. (doi:10.1016/S0169-7439(03)00086-8).

Record type: Article

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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More information

Published date: 28 October 2003
Keywords: Adjusted Wold's R criterion, Complexity, Durbin-Watson criterion, Monte Carlo Cross-Validation, PLS, Smoothing

Identifiers

Local EPrints ID: 414612
URI: http://eprints.soton.ac.uk/id/eprint/414612
ISSN: 0169-7439
PURE UUID: acbc7433-8b49-4b4d-9076-9da36ccabc8e
ORCID for S. Gourvénec: ORCID iD orcid.org/0000-0002-2628-7914

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Date deposited: 05 Oct 2017 16:30
Last modified: 10 Jan 2022 03:11

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Contributors

Author: S. Gourvénec ORCID iD
Author: J. A. Fernández Pierna
Author: D. L. Massart
Author: D. N. Rutledge

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