Shrinkage estimator

Summary

A shrinkage estimator is an estimator obtained by pulling a basic estimator toward a fixed target or a pooled value. Shrinking adds some bias but can lower the variance by more, so the mean squared error goes down. Examples are the James–Stein estimator, posterior means under normal priors, and penalized regression such as ridge regression.