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The idea behind the James-Stein estimator is to “shrink” individual sample means toward a central value (the grand mean), which reduces the overall estimation error. (View Highlight)

The general formula[3] for the James-Stein estimator is: Where: • x is the sample mean vector. • μ is the grand mean (the average of the sample means). • c is a shrinkage factor that lies between 0 and 1. It determines how much we pull the individual means toward the grand mean. (View Highlight)