Robust Detection of Multiple Outliers in Grouped Multivariate Data

Chrys Caroni and Nedret Billor

Journal of Applied Statistics, 2007, vol. 34, issue 10, pages 1241-1250

Abstract: Many methods have been developed for detecting multiple outliers in a single multivariate sample, but very few for the case where there may be groups in the data. We propose a method of simultaneously determining groups (as in cluster analysis) and detecting outliers, which are points that are distant from every group. Our method is an adaptation of the BACON algorithm proposed by Billor, Hadi and Velleman for the robust detection of multiple outliers in a single group of multivariate data. There are two versions of our method, depending on whether or not the groups can be assumed to have equal covariance matrices. The effectiveness of the method is illustrated by its application to two real data sets and further shown by a simulation study for different sample sizes and dimensions for 2 and 3 groups, with and without planted outliers in the data. When the number of groups is not known in advance, the algorithm could be used as a robust method of cluster analysis, by running it for various numbers of groups and choosing the best solution.

Keywords: Multivariate data; outliers; robust methods; BACON; cluster analysis (search for similar items in EconPapers)

Downloads: (external link)
http://www.informawo ... 40C6AD35DC6213A474B5 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Ordering information: This journal article can be ordered from
http://www.tandf.co.uk/journals/subscription.html

Access Statistics for this article

Journal of Applied Statistics is edited by Professor Gopal K. Kanji

More articles in Journal of Applied Statistics from Taylor and Francis Journals
Series data maintained by Christopher F. Baum ().