The covariance matrix obtained from the minimum covariance determinant estimator (Rousseeuw & Van Driessen) underestimates the true covariance matrix because it eliminates distant points. Is there a way to compensate for this so that cut-offs (Hotelling t^2) based on distance metrics such as the Mahalanobis distance give more accurate confidence intervals?

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