Understanding Shrinkage Estimators: From Zero to Oracle to James-Stein
36 Pages Posted: 17 Oct 2015
Date Written: October 17, 2015
The standard estimator of the population mean is the sample mean, which is unbiased. Constructing an estimator by shrinking the sample mean results in a biased estimator, with an expected value less than the population mean. On the other hand, shrinkage always reduces the estimator's variance and can reduce its mean squared error. This paper tries to explain how that works. I start with estimating a single mean using the zero estimator and the oracle estimator, and continue with the grand-mean estimator. Thus prepared, it is easier to understand the James-Stein estimator, in its simple form with known homogeneous variance and in extensions. The James-Stein estimator combines the oracle estimate's coefficient shrinking with the grand mean estimator's cancelling out of overestimates and underestimates.
Keywords: James Stein estimator, shrinkage estimator, oracle estimator, admissibility
JEL Classification: C13
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