A New Approach to Robust Partial Least Squares Regression Analysis

Esra Polat; Suleyman Gunay

doi:https://doi.org/10.14445/22315373/IJMTT-V9P524

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 9 | Number 3 | Year 2014 | Article Id. IJMTT-V9P524 | DOI : https://doi.org/10.14445/22315373/IJMTT-V9P524

A New Approach to Robust Partial Least Squares Regression Analysis

Esra Polat , Suleyman Gunay

Abstract

Partial Least Squares Regression (PLSR) is a linear regression technique developed to relate many independent variables to one or several dependent variables. Robust methods are introduced to reduce or remove the effects of outlying data points. In the previous studies in robust PLSR field it has been mentioned that if the sample covariance matrix is properly robustified further robustification of the linear regression steps of the PLS1 algorithm (PLSR with univariate dependent variable) becomes unnecessary. Therefore, the purpose of this study is to propose a new approach to robust PLSR based on statistical procedures for covariance matrix robustification by selecting the well-known S-estimators. Both simulation results and an analysis on a real data set, which is used in robust PLSR literature frequently, showing the effectiveness, success in fitting to regular data points and predictive power of the new proposed robust PLSR method.

Keywords

robust partial least squares regression, robust covariance matrix, S-estimators, goodness-of-fit, prediction, efficiency.

References

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Citation :

Esra Polat , Suleyman Gunay, "A New Approach to Robust Partial Least Squares Regression Analysis," International Journal of Mathematics Trends and Technology (IJMTT), vol. 9, no. 3, pp. 197-205, 2014. Crossref, https://doi.org/10.14445/22315373/IJMTT-V9P524