Review of robust multivariate statistical methods in high dimension

Peter Filzmoser; Valentin Todorov

doi:10.1016/j.aca.2011.03.055

Review of robust multivariate statistical methods in high dimension

Anal Chim Acta. 2011 Oct 31;705(1-2):2-14. doi: 10.1016/j.aca.2011.03.055. Epub 2011 Apr 9.

Authors

Peter Filzmoser¹, Valentin Todorov

Affiliation

¹ Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria. p.filzmoser@tuwien.ac.at

PMID: 21962341
DOI: 10.1016/j.aca.2011.03.055

Abstract

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.