Minimum-variance unbiased estimator

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

The question of determining the UMVUE, if one exists, for a particular problem is important for practical statistics, 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 the particular specification of "optimal" here — requiring unbiasedness and measuring "goodness" using the variance — may not always be what is wanted for any given practical situation, it is one where useful and generally applicable results can be found.

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Definition

Consider estimation of g(\theta) based on data X_1, X_2, \ldots, X_n i.i.d. from some member of a family of densities p_\theta, \theta \in \Omega, where \Omega is the parameter space. An unbiased estimator \delta(X_1, X_2, \ldots, X_n) of g(\theta) is UMVU if \forall \theta \in \Omega,

\mathrm{var}(\delta(X_1, X_2, \ldots, X_n)) \leq \mathrm{var}(\tilde{\delta}(X_1, X_2, \ldots, X_n))

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

If an unbiased estimator of 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 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 UMVU estimator.

Put formally, suppose \delta(X_1, X_2, \ldots, X_n) is unbiased for g(\theta), and that T is a complete sufficient statistic for the family of densities. Then

\eta(X_1, X_2, \ldots, X_n) = \mathrm{E}(\delta(X_1, X_2, \ldots, X_n)|T)\,

is the MVUE for g(\theta).

A Bayesian analog is a Bayes estimator, particularly with minimum mean square error (MMSE).

Estimator selection

An efficient estimator need not exist, but if it does and if it unbiased, it is the MVUE. Since the mean squared error (MSE) of an estimator δ is

\operatorname{MSE}(\delta) = \mathrm{var}(\delta) %2B[ \mathrm{bias}(\delta)]^{2}\

the MVUE minimizes MSE among unbiased estimators. In some cases biased estimators have lower MSE because they have a smaller variance than does any unbiased estimator; see estimator bias.

Example

Consider the data to be a single observation from an absolutely continuous distribution on \mathbb{R} with density

p_\theta(x) = \frac{ \theta e^{-x} }{(1 %2B e^{-x})^{\theta %2B 1} }

and we wish to find the UMVU estimator of

g(\theta) = \frac{1}{\theta^{2}}

First we recognize that the density can be written as

\frac{ e^{-x} } { 1 %2B e^{-x} } \exp( -\theta \log(1 %2B e^{-x}) %2B \log(\theta))

Which is an exponential family with sufficient statistic T = \mathrm{log}(1 %2B e^{-x}). In fact this is a full rank exponential family, and therefore T is complete sufficient. See exponential family for a derivation which shows

\mathrm{E}(T) = \frac{1}{\theta},\quad \mathrm{var}(T) = \frac{1}{\theta^{2}}

Therefore

\mathrm{E}(T^2) = \frac{2}{\theta^{2}}

Clearly \delta(X) = \frac{T^2}{2} is unbiased, thus the UMVU estimator is

\eta(X) = \mathrm{E}(\delta(X) | T) = \mathrm{E} \left( \left. \frac{T^2}{2} \,\right|\, T \right) = \frac{T^{2}}{2} = \frac{\log(1 %2B e^{-X})^{2}}{2}

This example illustrates that an unbiased function of the complete sufficient statistic will be UMVU.

Other examples

\frac{k%2B1}{k} m - 1,
where m is the sample maximum. This is a scaled and shifted (so unbiased) transform of the sample maximum, which is a sufficient and complete statistic. See German tank problem for details.

See also

Bayesian analogs

References