Abstract
The paper presents a number of empirical results concerning the detection of outliers via the Stahel-Donoho robust multivariate estimator (SDE). Specifically, the strategy put forward by Maronna and Yohai (1995) is deployed in various artificial data sets of relatively large size and dimensionality. It is found that (i) mild outliers tend to pose the most serious problems to the SDE with Huber weights; (ii) these problems are not due to the approximation method, they are intrinsic to the SDE; (iii) outliers can manifest at the lower tail of the distribution of scaled Mahalanobis distances (all outliers detected by Maronna and Yohai are located in the upper[ILL]); and (iv) the detection of outliers located at one corner of the data set may be complicated by the existence of a second outlying patch in the cloud (that is, the SDE is prone to suffer from the problem of masking). Overall, these results suggest that use of the SDE for outlier detection must be complemented with some visual analysis of the data cloud.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Communications in Statistics - Simulation and Computation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.