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2018 Robust Nonlinear Partial Least Squares Regression Using the BACON Algorithm
Abdelmounaim Kerkri, Jelloul Allal, Zoubir Zarrouk
J. Appl. Math. 2018: 1-5 (2018). DOI: 10.1155/2018/7696302

Abstract

Partial least squares regression (PLS regression) is used as an alternative for ordinary least squares regression in the presence of multicollinearity. This occurrence is common in chemical engineering problems. In addition to the linear form of PLS, there are other versions that are based on a nonlinear approach, such as the quadratic PLS (QPLS2). The difference between QPLS2 and the regular PLS algorithm is the use of quadratic regression instead of OLS regression in the calculations of latent variables. In this paper we propose a robust version of QPLS2 to overcome sensitivity to outliers using the Blocked Adaptive Computationally Efficient Outlier Nominators (BACON) algorithm. Our hybrid method is tested on both real and simulated data.

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Abdelmounaim Kerkri. Jelloul Allal. Zoubir Zarrouk. "Robust Nonlinear Partial Least Squares Regression Using the BACON Algorithm." J. Appl. Math. 2018 1 - 5, 2018. https://doi.org/10.1155/2018/7696302

Information

Received: 11 June 2018; Revised: 18 August 2018; Accepted: 18 September 2018; Published: 2018
First available in Project Euclid: 16 November 2018

zbMATH: 07051367
MathSciNet: MR3864618
Digital Object Identifier: 10.1155/2018/7696302

Rights: Copyright © 2018 Hindawi

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