unbiased estimator

In other words, is an unbiased estimator of the first moment.

In this case, the natural unbiased estimator is 2X 1.

The sample mean will serve as an unbiased estimator of the population mean.

In statistics, a statistic is called complete if it does not allow an unbiased estimator of zero.

The theorem holds regardless of whether biased or unbiased estimators are used.

This notion of efficiency is restricted to the class of unbiased estimators.

An unbiased estimator which achieves this lower bound is said to be (fully) efficient.

This implies that is an unbiased estimator for F(t).

Therefore the maximum likelihood estimate is an unbiased estimator of λ.

For small samples, it is customary to use an unbiased estimator for the population variance.

An approximately unbiased estimator can be obtained by solving the equation for .

For more details, the general theory of unbiased estimators is briefly discussed near the end of this article.

The kriging estimation is the best linear unbiased estimator of if the assumptions hold.

By the result above, any unbiased estimator whose expectation is has variance greater than or equal to .

He developed U-statistics, known as unbiased estimator statistics.

Additionally, the model is an unbiased estimator: the slopes are 1, and the intercepts are 0.

Nor are modern economists, raised on a limited diet of rational expectations theory and unbiased estimators, inclined to examine the big picture.

In particular, for an unbiased estimator, the variance equals the MSE.

For normally distributed data, an unbiased estimator for a sample of size n is:

An unbiased estimator of σ can be obtained by dividing s by c(n).

Mathematically speaking, the expected value of the unbiased estimator will always be the correct value, for any number of samples.

If represents the number of observations having the value , then is an unbiased estimator of the true .

Consider an unbiased estimator .

There does not exist a sample statistic that is in general an unbiased estimator of the population Gini coefficient, like the relative mean difference.

This theorem establishes optimality only in the class of linear unbiased estimators, which is quite restrictive.