On robust mahalanobis distance issued from minimum vector variance
Detecting outliers in high dimension datasets remains a challenging task.Under this circumstance, robust location and scale estimators are usually proposed in place of the classical estimators. Recently, a new robust estimator for multivariate data known as minimum variance vector (MVV) was introduc...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Pushpa Publishing House
2013
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/21569/1/FJMS%2074%202%202013%20249%20268.pdf http://repo.uum.edu.my/21569/ http://www.pphmj.com/abstract/7503.htm |
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| Summary: | Detecting outliers in high dimension datasets remains a challenging task.Under this circumstance, robust location and scale estimators are usually proposed in place of the classical estimators. Recently, a new robust estimator for multivariate data known as minimum variance vector (MVV) was introduced. Besides inheriting the nice properties of the famous MCD estimator, MVV is computationally more efficient. This paper proposes MVV to detect outliers via Mahalanobis squared distance (MSD).The results revealed that MVV is more effective in detecting outliers and in controlling Type I error compared with MCD. |
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