A new robust estimator to detect outliers for multivariate data
Mahalanobis distance (MD) is a classical method to detect outliers for multivariate data. However, classical mean and covariance matrix in MD suffered from masking and swamping effect if the data contain outliers. Due to this problem, many studies used robust estimator instead of the classical estim...
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| Format: | Conference or Workshop Item |
| Language: | English |
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IOP Publishing
2019
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| Online Access: | http://umpir.ump.edu.my/id/eprint/27847/ http://umpir.ump.edu.my/id/eprint/27847/ http://umpir.ump.edu.my/id/eprint/27847/1/A%20new%20robust%20estimator%20to%20detect%20outliers%20for%20multivariate%20data.pdf |
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ump-278472020-02-26T07:05:15Z http://umpir.ump.edu.my/id/eprint/27847/ A new robust estimator to detect outliers for multivariate data Sharifah Sakinah, Syed Abd Mutalib Siti Zanariah, Satari Wan Nur Syahidah, Wan Yusoff QA Mathematics Mahalanobis distance (MD) is a classical method to detect outliers for multivariate data. However, classical mean and covariance matrix in MD suffered from masking and swamping effect if the data contain outliers. Due to this problem, many studies used robust estimator instead of the classical estimator of mean and covariance matrix. In this study, a new robust estimator, namely, Test on Covariance (TOC) is proposed to detect outliers in multivariate data. The performance of TOC is compared with the existing robust estimators which are Fast Minimum Covariance Determinant (FMCD), Minimum Vector Variance (MVV), Covariance Matrix Equality (CME) and Index Set Equality (ISE). The probability that all the planted outliers are successfully detected (pout), probability of masking (pmask) and probability of swamping (pswamp) are computed for each estimator via simulation study. It is found that the TOC is applicable and a promising approach to detect the outliers for multivariate data. IOP Publishing 2019-11-07 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/27847/1/A%20new%20robust%20estimator%20to%20detect%20outliers%20for%20multivariate%20data.pdf Sharifah Sakinah, Syed Abd Mutalib and Siti Zanariah, Satari and Wan Nur Syahidah, Wan Yusoff (2019) A new robust estimator to detect outliers for multivariate data. In: Journal of Physics: Conference Series; 2nd International Conference on Applied and Industrial Mathematics and Statistics 2019, ICoAIMS 2019, 23 - 25 July 2019 , The Zenith Hotel, Kuantan, Pahang. pp. 1-10., 1366 (1). ISSN 1742-6588 (print); 1742-6596 (online) https://doi.org/10.1088/1742-6596/1366/1/012104 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Malaysia Pahang |
| building |
UMP Institutional Repository |
| collection |
Online Access |
| language |
English |
| topic |
QA Mathematics |
| spellingShingle |
QA Mathematics Sharifah Sakinah, Syed Abd Mutalib Siti Zanariah, Satari Wan Nur Syahidah, Wan Yusoff A new robust estimator to detect outliers for multivariate data |
| description |
Mahalanobis distance (MD) is a classical method to detect outliers for multivariate data. However, classical mean and covariance matrix in MD suffered from masking and swamping effect if the data contain outliers. Due to this problem, many studies used robust estimator instead of the classical estimator of mean and covariance matrix. In this study, a new robust estimator, namely, Test on Covariance (TOC) is proposed to detect outliers in multivariate data. The performance of TOC is compared with the existing robust estimators which are Fast Minimum Covariance Determinant (FMCD), Minimum Vector Variance (MVV), Covariance Matrix Equality (CME) and Index Set Equality (ISE). The probability that all the planted outliers are successfully detected (pout), probability of masking (pmask) and probability of swamping (pswamp) are computed for each estimator via simulation study. It is found that the TOC is applicable and a promising approach to detect the outliers for multivariate data. |
| format |
Conference or Workshop Item |
| author |
Sharifah Sakinah, Syed Abd Mutalib Siti Zanariah, Satari Wan Nur Syahidah, Wan Yusoff |
| author_facet |
Sharifah Sakinah, Syed Abd Mutalib Siti Zanariah, Satari Wan Nur Syahidah, Wan Yusoff |
| author_sort |
Sharifah Sakinah, Syed Abd Mutalib |
| title |
A new robust estimator to detect outliers for multivariate data |
| title_short |
A new robust estimator to detect outliers for multivariate data |
| title_full |
A new robust estimator to detect outliers for multivariate data |
| title_fullStr |
A new robust estimator to detect outliers for multivariate data |
| title_full_unstemmed |
A new robust estimator to detect outliers for multivariate data |
| title_sort |
new robust estimator to detect outliers for multivariate data |
| publisher |
IOP Publishing |
| publishDate |
2019 |
| url |
http://umpir.ump.edu.my/id/eprint/27847/ http://umpir.ump.edu.my/id/eprint/27847/ http://umpir.ump.edu.my/id/eprint/27847/1/A%20new%20robust%20estimator%20to%20detect%20outliers%20for%20multivariate%20data.pdf |
| first_indexed |
2023-09-18T22:43:41Z |
| last_indexed |
2023-09-18T22:43:41Z |
| _version_ |
1777417082696105984 |