Robustness and Outlier Detection in Chemometrics

Abstract

In analytical chemistry, experimental data often contain outliers of one type or another. The most often used chemometrical/statistical techniques are sensitive to such outliers, and the results may be adversely affected by them. This paper presents an overview of robust chemometrical/statistical methods which search for the model fitted by the majority of the data, and hence are far less affected by outliers. As an extra benefit, we can then detect the outliers by their large deviation from the robust fit. We discuss robust procedures for estimating location and scatter, and for performing multiple linear regression, PCA, PCR, PLS, and classification. We also describe recent results concerning the robustness of Support Vector Machines, which are kernel-based methods for fitting non-linear models. Finally, we present robust approaches for the analysis of multiway data.