Robust methods for multivariate data analysis

Authors

  • S. Frosch Møller,

    Corresponding author
    1. Department of Seafood Research, Danish Institute for Fisheries Research, The Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
    • Department of Seafood Research, Danish Institute for Fisheries Research, The Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
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  • J. von Frese,

    1. Spectroscopy and Chemometrics Group, Quality and Technology, Department of Food Science, The Royal Veterinary and Agricultural University, DK-1958 Frederiksberg C, Denmark
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  • R. Bro

    1. Spectroscopy and Chemometrics Group, Quality and Technology, Department of Food Science, The Royal Veterinary and Agricultural University, DK-1958 Frederiksberg C, Denmark
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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.

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