Robust partial least squares regression—part III, outlier analysis and application studies

This paper is the third part of the work on robust partial least squares (RPLS) regression. The paper focuses on implementation issues for outlier detection and diagnosis. Furthermore, the paper introduces a numerically more efficient algorithm for determining the Stahel–Donoho estimator (SDE). This has been identified as a potential drawback of the new proposed RPLS algorithm, detailed in Part II of this work. Finally, a total of three application studies are presented which involve data recorded from (i) a calibration experiment (similar number of variables/observations), (ii) a distillation process for purifying benzene (considerably more observations than variables) and (iii) an experiment of a multi‐component concentration determination using Raman spectroscopy (considerably more variables than observations).