Outlier Detection Based on Robust Mahalanobis Distance and Its Application - Open Journal of Statistics

OJS > Vol.9 No.1, February 2019

Open Journal of Statistics

Volume 9, Issue 1 (February 2019)

ISSN Print: 2161-718X ISSN Online: 2161-7198

Google-based Impact Factor: 0.53 Citations

Outlier Detection Based on Robust Mahalanobis Distance and Its Application ()

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DOI: 10.4236/ojs.2019.91002 1,867 Downloads 8,103 Views Citations

Author(s)

Xu Li¹, Songren Deng¹, Lifang Li¹, Yunchuan Jiang^2*

Affiliation(s)

¹School of Economic, Jinan University, Guangzhou, China.
²Department of Anatomy, Division of Basic Medicine, YongZhou Vocational Technical College, Yongzhou, China.

ABSTRACT

Classical Mahalanobis distance is used as a method of detecting outliers, and is affected by outliers. Some robust Mahalanobis distance is proposed via the fast MCD estimator. However, the bias of the MCD estimator increases significantly as the dimension increases. In this paper, we propose the improved Mahalanobis distance based on a more robust Rocke estimator under high-dimensional data. The results of numerical simulation and empirical analysis show that our proposed method can better detect the outliers in the data than the above two methods when there are outliers in the data and the dimensions of data are very high.

KEYWORDS

MCD Estimator, Rocke Estimator, Outlier, Mahalanobis Distance

Share and Cite:

Li, X. , Deng, S. , Li, L. and Jiang, Y. (2019) Outlier Detection Based on Robust Mahalanobis Distance and Its Application. Open Journal of Statistics, 9, 15-26. doi: 10.4236/ojs.2019.91002.

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