James–Stein estimator

Summary

The James–Stein estimator estimates the mean vector of a multivariate normal distribution in dimension $p \ge 3$ by shrinking the observation toward a fixed point. Under squared error loss its risk is strictly smaller than that of the observation itself, for every value of the mean. It is the standard illustration of Stein's example and the prototype of shrinkage estimators.