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Higher Order Asymptotic Efficiency and Asymptotic Completeness
1981In 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 Relative Efficiency
1981In any given inference situation, many statistical procedures may be available, both parametric and nonparametric. Some measure of their relative merits is needed, especially as regards their performance or operating characteristics. For instance, a comparison of the power functions of various tests of the same (or essentially the same) hypotheses ...
John W. Pratt, Jean D. Gibbons
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On the asymptotic efficiency of estimators
Mathematica Applicanda, 2018We 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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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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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, 1986openaire +2 more sources
The Concept of Asymptotic Efficiency
1988Partly 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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Second order asymptotic efficiency in a partial linear model
Statistics and Probability Letters, 1993Cheng Ping
exaly