International Journal of Statistics and Applied Mathematics
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International Journal of Statistics and Applied Mathematics

2017, Vol. 2, Issue 6, Part D

Preference for a class of super-efficient estimators of the normal mean: A study on sample size requirement


Author(s): K Sivasakthi, R Sakthivel and Martin L William

Abstract: A class of super-efficient estimators of the mean of a normal population with unit variance has been recently constructed by Sivasakthi et al. (2016) through the ‘Delta Method’. Theoretically, a super-efficient estimator is preferable to the asymptotically efficient estimator (could be the maximum likelihood estimator) in a large-sample context. In this paper, we address the super-efficient estimation of the normal mean when the population variance is known. The important question on the sample size required for a super-efficient estimator to be preferred over the (asymptotically) efficient estimator / maximum likelihood estimator is addressed through a numerical study. The answer to the question is sought for a chosen subset of the class of super-efficient estimators under consideration.

Pages: 241-249 | Views: 548 | Downloads: 25

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How to cite this article:
K Sivasakthi, R Sakthivel, Martin L William. Preference for a class of super-efficient estimators of the normal mean: A study on sample size requirement. Int J Stat Appl Math 2017;2(6):241-249.
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