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On the asymptotic efficiency of estimators

Mathematica Applicanda, 2018
We present and discuss the notion of asymptotic efficiency of estimators as introduced by Hajek and Le Cam. We give also some general construction of a class of asymptotically efficient estimators of Euclidean parameters. Moreover, we briefly indicate some generalizations of the discussed ideas to the case of semiparametric models.
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Higher Order Asymptotic Efficiency and Asymptotic Completeness

1981
In this chapter the concept of (higher order) asymptotic completeness of an estimator is introduced and it is shown that the maximum likelihood estimator is second order and third order asymptotically complete. Further the second order asymptotic efficiency of unbiased confidence intervals is discussed.
Masafumi Akahira, Kei Takeuchi
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Asymptotic Efficiency Bounds

2001
Any consistent and asymptotically normal estimator with a variance-covariance matrix of the stabilizing transformation attaining the Cramer-Rao efficiency bound is said to be asymptotically efficient (cf. Amemiya, 1985, p. 124). It is well known that the Cramer-Rao bound is given by the inverse of the information matrix.
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Asymptotic Relative Efficiency

The American Journal of Occupational Therapy, 1986
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The Concept of Asymptotic Efficiency

1988
Partly of an expository nature this note brings out the fact that an estimator, though asymptotically much less efficient (in the classical sense) than another, may yet have much greater probability concentration (as defined in this article) than the latter.
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Asymptotic Efficiency

1981
Masafumi Akahira, Kei Takeuchi
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Asymptotic Efficiency

1993
Per Kragh Andersen   +3 more
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Asymptotic relative efficiency

2012
The purpose of asymptotic relative efficiency is to compare two statistical procedures by comparing the sample sizes, n1 and n2, say, at which those procedures achieve some given measure of performance; the ratio n2/n1 is called the relative efficiency of procedure one with respect to procedure two.
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