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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Institution:	Universiti Utara Malaysia
Language:	English

id	my.uum.repo.21569
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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

On robust mahalanobis distance issued from minimum vector variance

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