Regular estimator

Template:Short description Regular estimators are a class of statistical estimators that satisfy certain regularity conditions which make them amenable to asymptotic analysis. The convergence of a regular estimator's distribution is, in a sense, locally uniform. This is often considered desirable and leads to the convenient property that a small change in the parameter does not dramatically change the distribution of the estimator.

Definition

An estimator ${\hat{\theta }}_n$ of $\psi (\theta )$ based on a sample of size $n$ is said to be regular if for every $h$:

$$\sqrt{n}({\hat{\theta }}_n-\psi (\theta +h/\sqrt{n}))\stackrel{\theta +h/\sqrt{n}}{\to }{L}_{\theta }$$

where the convergence is in distribution under the law of $\theta +h/\sqrt{n}$. $L_{\theta }$ is some asymptotic distribution (usually this is a normal distribution with mean zero and variance which may depend on $\theta$).

Examples of non-regular estimators

Both the Hodges' estimator and the James-Stein estimator are non-regular estimators when the population parameter $\theta$ is exactly 0.

See also

• Estimator
• Cramér-Rao bound
• Hodges' estimator
• James-Stein estimator