On the mean squared error of the ridge estimator of the covariance and precision matrix

Wessel N. van Wieringen

Statistics & Probability Letters, 2017, vol. 123, issue C, 88-92

Abstract: For a suitably chosen ridge penalty parameter, the ridge regression estimator uniformly dominates the maximum likelihood regression estimator in terms of the mean squared error. Analogous results for the ridge maximum likelihood estimators of covariance and precision matrix are presented.

Keywords: Inverse covariance matrix; Multivariate normal; ℓ2-penalization (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.spl.2016.12.002

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