Outlier detection for high-dimensional data

Kwangil Ro, Changliang Zou, Zhaojun Wang and Guosheng Yin

Biometrika, 2015, vol. 102, issue 3, 589-599

Abstract: Outlier detection is an integral component of statistical modelling and estimation. For high-dimensional data, classical methods based on the Mahalanobis distance are usually not applicable. We propose an outlier detection procedure that replaces the classical minimum covariance determinant estimator with a high-breakdown minimum diagonal product estimator. The cut-off value is obtained from the asymptotic distribution of the distance, which enables us to control the Type I error and deliver robust outlier detection. Simulation studies show that the proposed method behaves well for high-dimensional data.

Date: 2015
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Citations: View citations in EconPapers (8)

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