On Efficient Iterative Estimation Algorithm Using Sample Counterpart of the Searles’ Normal Mean Estimator
by By Winston A. Richards, Robin Antoine, Ashok Sahai, and M.
This paper addresses the issue of finding an optimal estimator of the normal population mean when the
coefficient of variation is unknown. The paper proposes an “Efficient Iterative Estimation Algorithm Using
Sample Counterpart of the Searles’ Normal Mean Estimator”. The estimators per this strategy have no close
form, and hence are not amenable to any analytical study determining their relative efficiencies as
compared to the usual unbiased sample mean estimator. Nevertheless, we examine these relative efficiencies
of our proposed iterative estimators with respect to the usual unbiased estimator by means of an
illustrative numerical empirical study. The MATLAB 184.108.40.2061
MVUE, MMSE, Complete Sufficient Statistic, Numerical Study
Winston A. Richards, firstname.lastname@example.org
Robin M. Antoine, email@example.com
Ashok Sahai, firstname.lastname@example.org
Raghunadh M. Acharya, email@example.com
Ahmed H. Youssef, firstname.lastname@example.org
READING THE ARTICLE: You can read the article in
portable document (.pdf) format (371226 bytes.)
NOTE: The content of this article is the intellectual property of the authors, who retains all rights to future publication.
This page has been accessed 4454 times since January 20, 2009.
Return to the Home Page.