Robust multivariate methods in Chemometrics

TL;DR

This chapter introduces robust statistical methods tailored for chemometric applications, focusing on multivariate analysis techniques like PCA and PLS, and discusses their validation and extension to classification tasks.

Contribution

It develops robust alternatives for common multivariate chemometric methods and explains how to validate their uncertainty, enhancing reliability in chemometric analysis.

Findings

Robust PCA and PLS methods improve analysis stability.

Uncertainty quantification for robust estimates is demonstrated.

Extensions to classification enhance chemometric applications.

Abstract

This chapter presents an introduction to robust statistics with applications of a chemometric nature. Following a description of the basic ideas and concepts behind robust statistics, including how robust estimators can be conceived, the chapter builds up to the construction (and use) of robust alternatives for some methods for multivariate analysis frequently used in chemometrics, such as principal component analysis and partial least squares. The chapter then provides an insight into how these robust methods can be used or extended to classification. To conclude, the issue of validation of the results is being addressed: it is shown how uncertainty statements associated with robust estimates, can be obtained.