… robust regression estimators have better properties. In this paper a “partial” version of M-regression …
scheme is chosen, partial M-estimators become entirely robust to any type of outlying …

… widely applied robust alternatives to PLS is partial robust M regression [8]. Likely its popularity
is due to the fact that it provides a fair tradeoff between statistical robustness with respect …

… the other robust PLSR methods: instead of robust partial least squares, a partial robust
regression … As the name suggests, it is a partial version of the robust M-regression. In an iterative …

… circumstance, a more robust regression method such as partial robust M-regression (PRM) …
In this paper, a kernel partial robust M-regression (KPRM) is presented. Nonlinear structure …

… Abstract—This paper introduces a modified partial robust Mregression approach. The
objective of the new approach is to improve the prediction accuracy of the regression model for …

… Based on the partial robust M-regression method, this paper first realizes a PRM-based …
Section 2 first reviews the basic algorithm of the partial robust M-regression and then proposes …

… problem could be reduced with the implementation of robust regression method. A recently
studied robust PLSR, which is called Partial Robust M-Regression (PRM) is found to be able …

… In view of the above problems, this paper applies Partial Robust M-regression (PRM) algorithm
to extreme learning machine, and an extreme learning machine based on calculating the …

The presence of Partial Robust M-Regression (PRM) amongst other Partial Least Squares
Regression (PLSR) techniques is mainly to offer a more robust and efficient method than the …

… Robust methods are developed to reduce or remove the effect of outliers. In this study,
robust PLSR is applied on a real data set with outliers and the results of Robust PLSR are …