| Summary: | Partial Robust M-Regression (PRM) is a robust Partial Least Squares (PLS) method using M-estimator, with multivariate L1 median and a monotonous weight function, known as Fair function in its algorithm. In many studies, the use of re-descending weight functions were much preferred to monotonous weight function due to the fact that the latter often failed to assign proper weights to outliers according to their severity. With the intention of improving the performance of PRM, this study suggested slight modifications to PRM by using winsorized mean and Hampel function, which comes from the family of re-descending weight functions. The proposed method was applied to a real high dimensional dataset which then modified to contain residual outliers as well as bad leverage points. The performance of PLS, PRM and modified PRM was assessed by means of their standard error of prediction (SEP) values. Compared to classical PLS and PRM, an improved performance was observed from the proposed method. © 2015 AIP Publishing LLC.
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