Title
Partial robust M-regression Partial robust M-regression
Author
Faculty/Department
Faculty of Sciences. Chemistry
Publication type
article
Publication
Amsterdam ,
Subject
Chemistry
Source (journal)
Chemometrics and intelligent laboratory systems. - Amsterdam
Volume/pages
79(2005) :1/2 , p. 55-64
ISSN
0169-7439
ISI
000232000300006
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Partial Least Squares (PLS) is a standard statistical method in chemometrics. It can be considered as an incomplete, or partial, version of the Least Squares estimator of regression, applicable when high or perfect multicollinearity is present in the predictor variables. The Least Squares estimator is well-known to be an optimal estimator for regression, but only when the error terms are normally distributed. In the absence of normality, and in particular when outliers are in the data set, other more robust regression estimators have better properties. In this paper a partial version of M-regression estimators will be defined. If an appropriate weighting scheme is chosen, partial M-estimators become entirely robust to any type of outlying points, and are called Partial Robust M-estimators. It is shown that partial robust M-regression outperforms existing methods for robust PLS regression in terms of statistical precision and computational speed, while keeping good robustness properties. The method is applied to a data set consisting of EPXMA spectra of archaeological glass vessels. This data set contains several outliers, and the advantages of partial robust M-regression are illustrated. Applying partial robust M-regression yields much smaller prediction errors for noisy calibration samples than PLS. On the other hand, if the data follow perfectly well a normal model, the loss in efficiency to be paid for is very small.
E-info
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