25 Pages Posted: 3 Feb 2017
Date Written: January 24, 2017
The Minimum Covariance Determinant (MCD) approach estimates the location and scatter matrix using the subset of given size with lowest sample covariance determinant. Its main drawback is that it cannot be applied when the dimension exceeds the subset size. We propose the Minimum Regularized Covariance Determinant (MRCD) approach, which differs from the MCD in that the subset-based covariance matrix is a convex combination of a target matrix and the sample covariance matrix. A data-driven procedure sets the weight of the target matrix, so that the regularization is only used when needed. The MRCD estimator is defined in any dimension, is well-conditioned by construction and preserves the good robustness properties of the MCD. We prove that so-called concentration steps can be performed to reduce the MRCD objective function, and we exploit this fact to construct a fast algorithm. We verify the accuracy and robustness of the MRCD estimator in a simulation study and illustrate its practical use for outlier detection and regression analysis on real-life high-dimensional data sets in chemistry and criminology.
Keywords: Breakdown point, High-dimensional data, Regularization, Robust covariance estimation
JEL Classification: C14, C32
Suggested Citation: Suggested Citation
Boudt, Kris and Rousseeuw, Peter and Vanduffel, Steven and Verdonck, Tim, The Minimum Regularized Covariance Determinant Estimator (January 24, 2017). Available at SSRN: https://ssrn.com/abstract=2905259