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May, 1977 Asymptotic Efficiency of Minimum Variance Unbiased Estimators
Stephen Portnoy
Ann. Statist. 5(3): 522-529 (May, 1977). DOI: 10.1214/aos/1176343849

Abstract

Consider a regular $p$-dimensional exponential family such that either the distributions are concentrated on a lattice or they have a component whose $k$-fold convolution has a bounded density with respect to Lebesgue measure. Then, if a parametric function has an unbiased estimator, the minimum variance unbiased estimators are asymptotically equivalent to the maximum likelihood estimators; and, hence, are asymptotically efficient. Examples are given to show that a condition like the above is needed to obtain the asymptotic equivalence.

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Stephen Portnoy. "Asymptotic Efficiency of Minimum Variance Unbiased Estimators." Ann. Statist. 5 (3) 522 - 529, May, 1977. https://doi.org/10.1214/aos/1176343849

Information

Published: May, 1977
First available in Project Euclid: 12 April 2007

zbMATH: 0356.62030
MathSciNet: MR436434
Digital Object Identifier: 10.1214/aos/1176343849

Subjects:
Primary: 62F20
Secondary: 62F10

Keywords: Asymptotic efficiency , exponential families , minimum variance unbiased estimators

Rights: Copyright © 1977 Institute of Mathematical Statistics

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Vol.5 • No. 3 • May, 1977
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