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In statistics, an estimator is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of data. Many different estimators are possible for any given parameter. Some criterion is used to choose between the estimators, although it is often the case that a criterion cannot be used to clearly pick one estimator over another.
For a point estimator θ of parameter θ:
- The bias of θ is defined as B(θ) = E[θ] − θ
- θ is an unbiased estimator of θ iff B(θ) = 0 for all θ
- The mean square error of θ is defined as MSE(θ) = E[(θ − θ)2]
- MSE(θ) = V(θ) + (B(θ))2
The standard deviation of an estimator of θ (the square root of the variance), or an estimate of the standard deviation of an estimator of θ, is called the standard error of θ.
A consistent estimator is an estimator that converges in probability to the quantity being estimated as the sample size grows.
An estimator tn (where n is the sample size) is a consistent estimator for parameter θ if and only if, for all ε > 0, no matter how small, we have
It is called strongly consistent, if it converges almost surely to the true value.
Occasionally one chooses the unbiased estimator with the lowest variance. Efficient estimators are those that have the lowest possible variance among all unbiased estimators. In some cases, a biased estimator may have a uniformly smaller mean squared error than does any unbiased estimator, so one should not make too much of this concept. For that and other reasons, it is sometimes preferable not to limit oneself to unbiased estimators; see bias (statistics). Concerning such "best unbiased estimators", see also Cramer-Rao inequality, Gauss-Markov theorem, Lehmann-Scheffé theorem, Rao-Blackwell theorem.
Often, estimator are due to restrictions (restricted estimators).
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