Detection of outliers in multivariate data: a method based on clustering and robust estimators

• Type: Article in International Conference Proceedings Book
• Date: 2002

Authors

Carla M. Santos Pereira (Author) FEUP; Ana M. Pires (Author) Other

Conference proceedings International

Title: Proceedings in Computational Statistics

Pages: 291-296

COMPSTAT

Berlim, 24 a 28 de Agosto

Indexing

ISI Web of Science

Scientific classification

FOS: Natural sciences

Other information

DOI: 10.1007/978-3-642-57489-4_41

Abstract (EN): Outlier identification is important in many applications of multivariate analysis. Either because there is some specific interest in finding anomalous observations or as a pre-processing task before the application of some multivariate method, in order to preserve the results from possible harmful effects of those observations. It is also of great interest in supervised classification (or 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 identification 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 effect (Rousseeuw and Leroy, 1985; Rousseeuw and von Zomeren, 1990; Rocke and Woodruff, 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 dependence.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6