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Robust methods for multivariate data analysis
Author(s) -
Møller S. Frosch,
von Frese J.,
Bro R.
Publication year - 2005
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.962
Subject(s) - chemometrics , outlier , multivariate statistics , robust regression , exploratory data analysis , robust statistics , computer science , multivariate analysis , statistics , field (mathematics) , robustness (evolution) , data mining , regression , regression analysis , principal component analysis , artificial intelligence , mathematics , machine learning , chemistry , biochemistry , gene , pure mathematics
Abstract 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.