Robust methods for multivariate data analysis A1

Stina Frosch, J. Von Frese, Rasmus Bro

Research output: Contribution to journalJournal articleResearchpeer-review


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.
Original languageEnglish
JournalJournal of Chemometrics
Issue number10
Pages (from-to)549-563
Publication statusPublished - 2005


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