Robust PCR and Robust PLSR: a Comparative Study

Abstract

Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR) are the two most popular regression techniques in chemometrics. They both fit a linear relationship between two sets of variables. The responses are usually low-dimensional whereas the regressors are very numerous compared to the number of observations. In this paper we compare two recent robust PCR and PLSR methods and their classical versions in terms of efficiency, goodness-of-fit, predictive power and robustness.