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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.There are two types of estimators: point estimators and interval estimators.
Point estimators
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
where V(X) is the variance of X and E is the expected value operator.
The standard deviation of θ (the square root of the variance) is also called the standard error of θ.
Occasionally one chooses the unbiased estimator with the lowest variance. Sometimes it is preferable not to limit oneself to unbiased estimators; see bias (statistics). Concerning such "best unbiased estimators", see also Gauss-Markov theorem, Lehmann-Scheffé theorem, Rao-Blackwell theorem.
- See also: maximum likelihood
