Mean squared error ($\mathrm{MSE}$)

The mean squared error of an estimator $\hat{\theta }$ is

If independent observations have mean $\theta$ and variance ${\theta }^2$, the scaled sample mean hasFor $\theta \ne 0$, it improves on the unbiased sample mean precisely when $(n-1)/(n+1)<k<1$. The optimal scale is $k=n/(n+1)$ and its risk is ${\theta }^2/(n+1)$. At $\theta =0$ all risks vanish and strict improvement is impossible.

The integrated mean squared error is $\mathbb{E}{\int }_D(\hat{m}(x)-m(x){)}^{2}dx$ over a specified domain $D$. The Tonelli theorem permits integration of the pointwise bias-variance decomposition of mean squared error.

Every estimator with finite second moment satisfies

For $X\sim \mathrm{Bin}(n,\theta )$ andthe mean squared error isWhen ${\theta }_0=1/2$, its maximal risk is at $\theta =1/2$ or at an endpoint of $[0,1]$.