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Estimator


An estimator is a rule that tells how to calculate an estimate based on the measurements contained in a sample. For example, the sample mean x^_ is an estimator for the population mean mu.

For a real-valued estimator theta^~ of a real number theta with <theta^~^2><infty, the mean square error is defined by

 MSE=<(theta^~-theta)^2>.
(1)

Here angle brackets denote the expectation value. Let B(theta^~)=<theta^~>-theta be the estimator bias. Then

MSE=<[(theta^~-<theta^~>)+B(theta^~)]^2>
(2)
=<(theta^~-<theta^~>)^2>+B^2(theta^~)
(3)
=V(theta^~)+B^2(theta^~),
(4)

where V is the estimator variance.


See also

Error, Estimate, Estimator Bias, Expectation Value, h-Statistic, k-Statistic, Minimum Variance Unbiased Estimator, Polyache, Polykay, Sample Central Moment, Sample Mean, Sample Variance, Unbiased Estimator

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Cite this as:

Weisstein, Eric W. "Estimator." From MathWorld--A Wolfram Resource. https://mathworld.wolfram.com/Estimator.html

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