An elementary introduction of Stein's estimator and its relation to compound Bayes and empirical Bayes estimation methods. Stein showed that, for the problem of estimating the mean of a normal distribution with squared-error loss, there exists a better estimator than the sample mean when the number of dimensions is at least three. Applications and the limitations of the estimator are discussed.
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