A new robust estimator to detect outliers for multivariate data

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)

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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.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Classical estimators; Classical methods; Mahalanobis distances; Multivariate data; Robust estimators; Simulation studies
Subjects: Q Science > QA Mathematics
Faculty/Division: Institute of Postgraduate Studies
Center for Mathematical Science
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 26 Feb 2020 07:05
Last Modified: 26 Feb 2020 07:05
URI: http://umpir.ump.edu.my/id/eprint/27847
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