Review of robust multivariate statistical methods in high dimension

General ideas of robust statistics, and specifically robust statistical methods for calibration and dimension reduction are discussed. The emphasis is on analyzing high-dimensional data. The discussed methods are applied using the packages chemometrics and rrcov of the statistical software environment R. It is demonstrated how the functions can be applied to real high-dimensional data from chemometrics, and how the results can be interpreted.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► We explain the main concepts of robust statistics. ► We focus on robust estimation in high dimension. ► Robust regression, robust PLS, and robust PCA are considered. ► R code for computation is shown and discussed for real data examples.