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has been cited by the following article:

• TITLE: Outlier Detection Based on Robust Mahalanobis Distance and Its Application

  AUTHORS: Xu Li, Songren Deng, Lifang Li, Yunchuan Jiang

  KEYWORDS: MCD Estimator, Rocke Estimator, Outlier, Mahalanobis Distance

  JOURNAL NAME: Open Journal of Statistics, Vol.9 No.1, January 24, 2019

  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.