DMCA
Nonparametric Depth-Based Multivariate Outlier Identifiers, and Robustness Properties,” submitted for journal publication (2006)
| Citations: | 12 - 7 self |
BibTeX
@MISC{Dang06nonparametricdepth-based,
author = {Xin Dang and Robert Serfling},
title = {Nonparametric Depth-Based Multivariate Outlier Identifiers, and Robustness Properties,” submitted for journal publication},
year = {2006}
}
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Abstract
In extending univariate outlier detection methods to higher dimension, various issues arise: limited visualization methods, inadequacy of marginal methods, lack of a natural order, limited parametric modeling, and, when using Mahalanobis distance, restriction to ellipsoidal contours. To address and overcome such limitations, we introduce nonparametric multivariate outlier identifiers based on multivariate depth functions, which can generate contours following the shape of the data set. Also, we study masking robustness, that is, robustness against misidentification of outliers as nonoutliers. In particular, we define a masking breakdown point (MBP), adapting to our setting
Keyphrases
robustness property nonparametric depth-based multivariate outlier identifier journal publication nonparametric multivariate outlier identifier breakdown point marginal method multivariate depth function detection method natural order data set limited parametric modeling various issue mahalanobis distance limited visualization method
