In statistics a minimum-variance unbiased estimator (MVUE) or uniformly minimum-variance unbiased estimator (UMVUE) is an unbiased estimator that has lower variance than any other unbiased estimator for all possible values of the parameter.

For practical statistics problems, it is important to determine the MVUE if one exists, since less-than-optimal procedures would naturally be avoided, other things being equal. This has led to substantial development of statistical theory related to the problem of optimal estimation.

While combining the constraint of unbiasedness with the desirability metric of least variance leads to good results in most practical settings—making MVUE a natural starting point for a broad range of analyses—a targeted specification may perform better for a given problem; thus, MVUE is not always the best stopping point.

Consider estimation of ${\displaystyle g(\theta )}$ based on data ${\displaystyle X_{1},X_{2},\ldots ,X_{n}}$ i.i.d. from some member of a family of densities ${\displaystyle p_{\theta },\theta \in \Omega }$, where ${\displaystyle \Omega }$ is the parameter space. An unbiased estimator ${\displaystyle \delta (X_{1},X_{2},\ldots ,X_{n})}$ of ${\displaystyle g(\theta )}$ is UMVUE if ${\displaystyle \forall \theta \in \Theta }$,

$${\displaystyle \operatorname {var} (\delta (X_{1},X_{2},\ldots ,X_{n}))\leq \operatorname {var} ({\tilde {\delta }}(X_{1},X_{2},\ldots ,X_{n}))}$$

for any other unbiased estimator ${\displaystyle {\tilde {\delta }}.}$

If an unbiased estimator of ${\displaystyle g(\theta )}$ exists, then one can prove there is an essentially unique MVUE. Using the Rao–Blackwell theorem one can also prove that determining the MVUE is simply a matter of finding a complete sufficient statistic for the family ${\displaystyle p_{\theta },\theta \in \Omega }$ and conditioning any unbiased estimator on it.

Further, by the Lehmann–Scheffé theorem, an unbiased estimator that is a function of a complete, sufficient statistic is the UMVUE estimator.