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Minimum Message Length shrinkage estimation

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  • Makalic, Enes
  • Schmidt, Daniel F.

Abstract

This note considers estimation of the mean of a multivariate Gaussian distribution with known variance within the Minimum Message Length (MML) framework. Interestingly, the resulting MML estimator exactly coincides with the positive-part James-Stein estimator under the choice of an uninformative prior. A new approach for estimating parameters and hyperparameters in general hierarchical Bayes models is also presented.

Suggested Citation

  • Makalic, Enes & Schmidt, Daniel F., 2009. "Minimum Message Length shrinkage estimation," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1155-1161, May.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:9:p:1155-1161
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    References listed on IDEAS

    as
    1. Hansen M. H & Yu B., 2001. "Model Selection and the Principle of Minimum Description Length," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 746-774, June.
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