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

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  • van Wieringen, Wessel N.

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.

Suggested Citation

  • van Wieringen, Wessel N., 2017. "On the mean squared error of the ridge estimator of the covariance and precision matrix," Statistics & Probability Letters, Elsevier, vol. 123(C), pages 88-92.
    • Handle: RePEc:eee:stapro:v:123:y:2017:i:c:p:88-92
    DOI: 10.1016/j.spl.2016.12.002
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    References listed on IDEAS

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    1. van Wieringen, Wessel N. & Peeters, Carel F.W., 2016. "Ridge estimation of inverse covariance matrices from high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 284-303.
    2. Gérard Letac & Hélène Massam, 2004. "All Invariant Moments of the Wishart Distribution," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(2), pages 295-318, June.
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    Cited by:

    1. van Wieringen, Wessel N. & Stam, Koen A. & Peeters, Carel F.W. & van de Wiel, Mark A., 2020. "Updating of the Gaussian graphical model through targeted penalized estimation," Journal of Multivariate Analysis, Elsevier, vol. 178(C).

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