 Minimumvariance unbiased estimator

In statistics a uniformly minimumvariance unbiased estimator or minimumvariance 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 lessthanoptimal 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.
Contents
Definition
Consider estimation of g(θ) based on data i.i.d. from some member of a family of densities , where Ω is the parameter space. An unbiased estimator of g(θ) is UMVU if ,
for any other unbiased estimator
If an unbiased estimator of g(θ) 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 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 is unbiased for g(θ), and that T is a complete sufficient statistic for the family of densities. Then
is the MVUE for g(θ).
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
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 with density
and we wish to find the UMVU estimator of
First we recognize that the density can be written as
Which is an exponential family with sufficient statistic T = log(1 + 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
Therefore
Clearly is unbiased, thus the UMVU estimator is
This example illustrates that an unbiased function of the complete sufficient statistic will be UMVU.
Other examples
 For a normal distribution with unknown mean and variance, the sample mean and (unbiased) sample variance are the MVUEs for the population mean and population variance.
 However, the sample standard deviation is not unbiased for the population standard deviation – see unbiased estimation of standard deviation.
 Further, for other distributions the sample mean and sample variance are not in general MVUEs – for a uniform distribution with unknown upper and lower bounds, the midrange is the MVUE for the population mean.
 If k exemplars are chosen (without replacement) from a discrete uniform distribution over the set {1, 2, ..., N} with unknown upper bound N, the MVUE for N is

 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
 Best linear unbiased estimator (BLUE)
 Lehmann–Scheffé theorem
 Ustatistic
Bayesian analogs
References
 Keener, Robert W. (2006). Statistical Theory: Notes for a Course in Theoretical Statistics. Springer. pp. 47–48, 57–58.
Categories:  For a normal distribution with unknown mean and variance, the sample mean and (unbiased) sample variance are the MVUEs for the population mean and population variance.
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