## Why doesn’t Stein effect happen for multinomial distributions?

(Medeen, et all, 1998)" show that Maximum Likelihood estimate is admissible for multinomial distribution under squared error. On other hand, James and Stein showed that arithmetic average is not an admissible estimator of Gaussian location parameter in 3 dimensions. But since maximum likelihood estimates of multinomial parameters are averages of observed counts, which become normally distributed for large sample sizes, why doesn't Stein effect happen here?

$\hat{p}$ is an inadmissible estimator of $\theta$ if there's an estimator that is no worse for every $\theta$ and better for at least one

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 Minor comment: I believe that was James and Stein, not "James Stein". – Mark Meckes Sep 17 2010 at 1:01

In the case of the multinomial parameters, they inherently have dependence via the sum-to-one constraint as a probability vector and you take this into account when averaging over possible parameter values. So a question related to yours, which may shed some light on it, is whether or not the MLE is admissible for a Gaussian location vector $\mu$ under the restriction that $\|\mu\| = c$ for some positive constant $c$.