Stein's example is a concept in the field of statistics, particularly in the context of estimation theory. It refers to a specific case that illustrates the phenomenon of "Stein's paradox," which highlights situations where the optimal estimator can outperform the maximum likelihood estimator (MLE) even when the MLE is unbiased. The classic example involves estimating the mean of a multivariate normal distribution.
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