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In statistics a minimum-variance unbiased estimator ... Theoretical statistics: Topics for a core course. New York: Springer. DOI 10.1007/978-0-387-93839-4;
In statistics, the theory of minimum norm quadratic unbiased estimation (MINQUE) [1] [2] [3] was developed by C. R. Rao. MINQUE is a theory alongside other estimation methods in estimation theory , such as the method of moments or maximum likelihood estimation .
For example, the ML estimator from the previous example may be attained as the limit of Bayes estimators with respect to a uniform prior, [,] with increasing support and also with respect to a zero-mean normal prior (,) with increasing variance. So neither the resulting ML estimator is unique minimax nor the least favorable prior is unique.
In statistics, the method of estimating equations is a way of specifying how the parameters of a statistical model should be estimated. This can be thought of as a generalisation of many classical methods—the method of moments , least squares , and maximum likelihood —as well as some recent methods like M-estimators .
In statistics, the Lehmann–Scheffé theorem is a prominent statement, tying together the ideas of completeness, sufficiency, uniqueness, and best unbiased estimation. [1] The theorem states that any estimator that is unbiased for a given unknown quantity and that depends on the data only through a complete , sufficient statistic is the unique ...
In statistical theory, a U-statistic is a class of statistics defined as the average over the application of a given function applied to all tuples of a fixed size. The letter "U" stands for unbiased. [citation needed] In elementary statistics, U-statistics arise naturally in producing minimum-variance unbiased estimators.
Algorithms for calculating variance play a major role in computational statistics.A key difficulty in the design of good algorithms for this problem is that formulas for the variance may involve sums of squares, which can lead to numerical instability as well as to arithmetic overflow when dealing with large values.
Efficient estimators are always minimum variance unbiased estimators. However the converse is false: There exist point-estimation problems for which the minimum-variance mean-unbiased estimator is inefficient. [6] Historically, finite-sample efficiency was an early optimality criterion. However this criterion has some limitations: