Examples
The James–Stein estimator is a nonlinear estimator which can be shown to dominate, or outperform, the ordinary least squares technique with respect to a mean-square error loss function. Thus least squares estimation is not necessarily an admissible estimation procedure. Some others of the standard estimates associated with the normal distribution are also inadmissible: for example, the sample estimate of the variance when the population mean and variance are unknown.
Read more about this topic: Admissible Decision Rule
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