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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: Ali, Hazlina, Syed Yahaya, Sharipah Soaad
Format: Article
Language:English
Published: Pushpa Publishing House 2013
Subjects:
QA Mathematics
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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spelling my.uum.repo.215692017-04-16T02:23:53Z http://repo.uum.edu.my/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 http://repo.uum.edu.my/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
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Ali, Hazlina
Syed Yahaya, Sharipah Soaad
On robust mahalanobis distance issued from minimum vector variance
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.
format Article
author Ali, Hazlina
Syed Yahaya, Sharipah Soaad
author_facet Ali, Hazlina
Syed Yahaya, Sharipah Soaad
author_sort Ali, Hazlina
title On robust mahalanobis distance issued from minimum vector variance
title_short 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_sort on robust mahalanobis distance issued from minimum vector variance
publisher Pushpa Publishing House
publishDate 2013
url 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
_version_ 1644283275089281024
score 13.154949

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