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  "body": "\n## Summary\n\nA shrinkage estimator is an estimator obtained by pulling a basic estimator toward a fixed target or a pooled value.\nShrinking adds some bias but can lower the variance by more, so the mean squared error goes down.\nExamples are the [[James–Stein estimator|709T196XCT]], posterior means under normal priors, and penalized regression such as [[ridge regression|3KZW2930V2]].\n\n## Explanations\n\n### L1 human\n\nRaw estimates computed from little data tend to be too extreme: some are too high and some too low just by chance.\nA shrinkage estimator moves each raw estimate part of the way toward a sensible central value.\nThe less reliable the raw estimate, the more it is moved.\nFor example, a batting average from only ten games is better predicted by moving it toward the league average than by taking it at face value.\n",
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