Spatial sign preprocessing: a simple way to impart moderate robustness to multivariate estimators.

Abstract:

:The spatial sign is a multivariate extension of the concept of sign. Recently multivariate estimators of covariance structures based on spatial signs have been examined by various authors. These new estimators are found to be robust to outlying observations. From a computational point of view, estimators based on spatial sign are very easy to implement as they boil down to a transformation of the data to their spatial signs, from which the classical estimator is then computed. Hence, one can also consider the transformation to spatial signs to be a preprocessing technique, which ensures that the calibration procedure as a whole is robust. In this paper, we examine the special case of spatial sign preprocessing in combination with partial least squares regression as the latter technique is frequently applied in the context of chemical data analysis. In a simulation study, we compare the performance of the spatial sign transformation to nontransformed data as well as to two robust counterparts of partial least squares regression. It turns out that the spatial sign transform is fairly efficient but has some undesirable bias properties. The method is applied to a recently published data set in the field of quantitative structure-activity relationships, where it is seen to perform equally well as the previously described best linear model for these data.

journal_name

J Chem Inf Model

authors

Serneels S,De Nolf E,Van Espen PJ

doi

10.1021/ci050498u

subject

Has Abstract

pub_date

2006-05-01 00:00:00

pages

1402-9

issue

3

eissn

1549-9596

issn

1549-960X

journal_volume

46

pub_type

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