The L-Curve Criterion as a Model Selection Tool in PLS Regression
Partial least squares (PLS) regression is an alternative to the ordinary least squares (OLS) regression, used in the presence of multicollinearity. As with any other modelling method, PLS regression requires a reliable model selection tool. Cross validation (CV) is the most commonly used tool with m...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2019/3129769 |
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| author | Abdelmounaim Kerkri Jelloul Allal Zoubir Zarrouk |
| author_facet | Abdelmounaim Kerkri Jelloul Allal Zoubir Zarrouk |
| author_sort | Abdelmounaim Kerkri |
| collection | DOAJ |
| description | Partial least squares (PLS) regression is an alternative to the ordinary least squares (OLS) regression, used in the presence of multicollinearity. As with any other modelling method, PLS regression requires a reliable model selection tool. Cross validation (CV) is the most commonly used tool with many advantages in both preciseness and accuracy, but it also has some drawbacks; therefore, we will use L-curve criterion as an alternative, given that it takes into consideration the shrinking nature of PLS. A theoretical justification for the use of L-curve criterion is presented as well as an application on both simulated and real data. The application shows how this criterion generally outperforms cross validation and generalized cross validation (GCV) in mean squared prediction error and computational efficiency. |
| format | Article |
| id | doaj-art-6e68e61851ec4bf28a4b9f504b65326b |
| institution | OA Journals |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-6e68e61851ec4bf28a4b9f504b65326b2025-08-20T02:06:28ZengWileyJournal of Probability and Statistics1687-952X1687-95382019-01-01201910.1155/2019/31297693129769The L-Curve Criterion as a Model Selection Tool in PLS RegressionAbdelmounaim Kerkri0Jelloul Allal1Zoubir Zarrouk2Laboratory of Stochastic and Deterministic Modelling (LMSD), Department of Mathematics, Faculty of Sciences, University Mohamed the First, Oujda 60000, MoroccoLaboratory of Stochastic and Deterministic Modelling (LMSD), Department of Mathematics, Faculty of Sciences, University Mohamed the First, Oujda 60000, MoroccoLaboratory of Management and Economy of Organizations, Faculty of Social Sciences, University Mohamed the First, Oujda 60000, MoroccoPartial least squares (PLS) regression is an alternative to the ordinary least squares (OLS) regression, used in the presence of multicollinearity. As with any other modelling method, PLS regression requires a reliable model selection tool. Cross validation (CV) is the most commonly used tool with many advantages in both preciseness and accuracy, but it also has some drawbacks; therefore, we will use L-curve criterion as an alternative, given that it takes into consideration the shrinking nature of PLS. A theoretical justification for the use of L-curve criterion is presented as well as an application on both simulated and real data. The application shows how this criterion generally outperforms cross validation and generalized cross validation (GCV) in mean squared prediction error and computational efficiency.http://dx.doi.org/10.1155/2019/3129769 |
| spellingShingle | Abdelmounaim Kerkri Jelloul Allal Zoubir Zarrouk The L-Curve Criterion as a Model Selection Tool in PLS Regression Journal of Probability and Statistics |
| title | The L-Curve Criterion as a Model Selection Tool in PLS Regression |
| title_full | The L-Curve Criterion as a Model Selection Tool in PLS Regression |
| title_fullStr | The L-Curve Criterion as a Model Selection Tool in PLS Regression |
| title_full_unstemmed | The L-Curve Criterion as a Model Selection Tool in PLS Regression |
| title_short | The L-Curve Criterion as a Model Selection Tool in PLS Regression |
| title_sort | l curve criterion as a model selection tool in pls regression |
| url | http://dx.doi.org/10.1155/2019/3129769 |
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