Nonsingular Robust Covariance Estimation in Multivariate Outlier Detection
نویسندگان
چکیده
Rousseeuw’s minimum covariance determinant (MCD) method is a highly robust estimator of multivariate mean and covariance. In practice, the MCD covariance estimator may be singular. However, a nonsingular covariance estimator is required to calculate the Mahalanobis distance. In order to fix this singular problem, we propose an improved version of the MCD estimator, which is a combination of the maximum likelihood estimator and the classical unbiased estimator. This estimator is nonsingular, robust, and as good as the MCD estimator with the same computational complexity.
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