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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Pushpa Publishing House
2013
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| Online Access: | https://repo.uum.edu.my/id/eprint/21569/1/FJMS%2074%202%202013%20249%20268.pdf |
| _version_ | 1825804584261517312 |
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| author | Ali, Hazlina Syed Yahaya, Sharipah Soaad |
| author_facet | Ali, Hazlina Syed Yahaya, Sharipah Soaad |
| author_sort | Ali, Hazlina |
| collection | UUM |
| description | 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. |
| first_indexed | 2024-07-04T06:17:56Z |
| format | Article |
| id | uum-21569 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:17:56Z |
| publishDate | 2013 |
| publisher | Pushpa Publishing House |
| record_format | eprints |
| spelling | uum-215692017-04-16T02:23:53Z https://repo.uum.edu.my/id/eprint/21569/ On robust mahalanobis distance issued from minimum vector variance Ali, Hazlina Syed Yahaya, Sharipah Soaad QA Mathematics 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. Pushpa Publishing House 2013 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/21569/1/FJMS%2074%202%202013%20249%20268.pdf Ali, Hazlina and Syed Yahaya, Sharipah Soaad (2013) On robust mahalanobis distance issued from minimum vector variance. Far East Journal of Mathematical Sciences (FJMS), 74 (2). pp. 249-268. ISSN 0972-0871 http://www.pphmj.com/abstract/7503.htm |
| spellingShingle | QA Mathematics Ali, Hazlina Syed Yahaya, Sharipah Soaad On robust mahalanobis distance issued from minimum vector variance |
| title | On robust mahalanobis distance issued from minimum vector variance |
| title_full | On robust mahalanobis distance issued from minimum vector variance |
| title_fullStr | On robust mahalanobis distance issued from minimum vector variance |
| title_full_unstemmed | On robust mahalanobis distance issued from minimum vector variance |
| title_short | On robust mahalanobis distance issued from minimum vector variance |
| title_sort | on robust mahalanobis distance issued from minimum vector variance |
| topic | QA Mathematics |
| url | https://repo.uum.edu.my/id/eprint/21569/1/FJMS%2074%202%202013%20249%20268.pdf |
| work_keys_str_mv | AT alihazlina onrobustmahalanobisdistanceissuedfromminimumvectorvariance AT syedyahayasharipahsoaad onrobustmahalanobisdistanceissuedfromminimumvectorvariance |