The L-Curve Criterion as a Model Selection Tool in PLS Regression. (30th October 2019)

• Record Type: Journal Article
• Authors: Kerkri, Abdelmounaim; Allal, Jelloul; Zarrouk, Zoubir
• Other Names: Gil-Alana Luis A. Academic Editor.
• Is Part Of: Journal of probability and statistics. Volume 2019(2019)
• Publication Date: 2019-10-30
• Subjects: Probabilities -- Periodicals; Mathematical statistics -- Periodicals; Mathematical statistics; Probabilities; Periodicals; 519
• DOI: 10.1155/2019/3129769
• Languages: English
• ISSNs: 1687-952X

Abstract

Abstract : 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.