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This work assesses the efficacy of Stein's estimator compared to the maximum likelihood estimator (MLE).

Stein's Estimation Rule and its Competitors—An Empirical Bayes Approach

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Numerical analysis investigates the performance of Stein's estimator versus competitors, indicating its effectiveness in estimation.

This work assesses the efficacy of Stein's estimator compared to the maximum likelihood estimator (MLE).
Utilizes empirical Bayes methods to analyze performance.
Compares Stein's estimator with other Bayesian rules for normal means estimation.
Explores applications in more complex estimation problems with non-normal distributions.
Stein's positive part estimator dominates the MLE for k ≥ 3, retaining good Bayesian properties.
Other Bayesian rules are identified as useful alternative estimators in specific scenarios.

Efron et al. (1973) studied this question.

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