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Robust Nonlinear Partial Least Squares Regression Using the BACON Algorithm
Joint Authors
Allal, Jelloul
Kerkri, Abdelmounaim
Zarrouk, Zoubir
Source
Journal of Applied Mathematics
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-10-02
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Kerkri, Abdelmounaim& Allal, Jelloul& Zarrouk, Zoubir. 2018. Robust Nonlinear Partial Least Squares Regression Using the BACON Algorithm. Journal of Applied Mathematics،Vol. 2018, no. 2018, pp.1-5.
https://search.emarefa.net/detail/BIM-1176056
Modern Language Association (MLA)
Kerkri, Abdelmounaim…[et al.]. Robust Nonlinear Partial Least Squares Regression Using the BACON Algorithm. Journal of Applied Mathematics No. 2018 (2018), pp.1-5.
https://search.emarefa.net/detail/BIM-1176056
American Medical Association (AMA)
Kerkri, Abdelmounaim& Allal, Jelloul& Zarrouk, Zoubir. Robust Nonlinear Partial Least Squares Regression Using the BACON Algorithm. Journal of Applied Mathematics. 2018. Vol. 2018, no. 2018, pp.1-5.
https://search.emarefa.net/detail/BIM-1176056
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1176056