Using clustering and robust estimators to detect outliers in multivariate data

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Author list: Pires, A. and Santos-Pereira, C.
Publication year: 2005
Languages: English-Great Britain (EN-GB)


Abstract

Introduction



Outlier identi¯cation is important in many applications of multivariate analysis. Either because

there is some speci¯c interest in ¯nding anomalous observations or as a pre-processing task before

the application of some multivariate method, in order to preserve the results from possible harmful

e®ects of those observations. It is also of great interest in discriminant analysis if, when predicting

group membership, one wants to have the possibility of labelling an observation as "does not belong

to any of the available groups". The identi¯cation of outliers in multivariate data is usually based

on Mahalanobis distance. The use of robust estimates of the mean and the covariance matrix

is advised in order to avoid the masking e®ect (Rousseeuw and von Zomeren, 1990; Rocke and

Woodru®, 1996; Becker and Gather, 1999). However, the performance of these rules is still highly

dependent of multivariate normality of the bulk of the data. The aim of the method here described is

to remove this dependency. The ¯rst version of this method appeared in Santos-Pereira and Pires

(2002). In this talk we discuss some re¯nements and also the relation with a recently proposed

similar method (Hardin and Rocke, 2004).


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Last updated on 2019-13-08 at 00:52