Mean Square Error

Given an estimator, $\hat{\theta }$, for the parameter $\theta$, we define the mean square error as

$$\text{mse}(\hat{\theta })=E[(\hat{\theta }-\theta {)}^2]$$

Rewriting this (look page=8), we get

$$\text{mse}(\hat{\theta })=\text{var}(\hat{\theta })+b^2(\theta )$$

Where $b=E[\hat{\theta }]-\theta$

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