Robust methods for multivariate data analysis
Article first published online: 5 MAY 2006
Copyright © 2006 John Wiley & Sons, Ltd.
Journal of Chemometrics
Volume 19, Issue 10, pages 549–563, October 2005
How to Cite
Møller, S. F., von Frese, J. and Bro, R. (2005), Robust methods for multivariate data analysis. J. Chemometrics, 19: 549–563. doi: 10.1002/cem.962
- Issue published online: 5 MAY 2006
- Article first published online: 5 MAY 2006
- Manuscript Accepted: 27 JAN 2006
- Manuscript Revised: 6 DEC 2005
- Manuscript Received: 24 FEB 2005
- Danish Ministry of Food, Agriculture and Fisheries
- robust estimation;
Outliers may hamper proper classical multivariate analysis, and lead to incorrect conclusions. To remedy the problem of outliers, robust methods are developed in statistics and chemometrics. Robust methods reduce or remove the effect of outlying data points and allow the ‘good’ data to primarily determine the result. This article reviews the most commonly used robust multivariate regression and exploratory methods that have appeared since 1996 in the field of chemometrics. Special emphasis is put on the robust versions of chemometric standard tools like PCA and PLS and the corresponding robust estimates of regression, location and scatter on which they are based. Copyright © 2006 John Wiley & Sons, Ltd.