1School of Statistics and Actuarial Science, University of the Witwatersrand, P Bag 3, Wits 2050, Republic of South Africa e-mail: honest.chipoyera@wits.ac.za or hwchipoyera@gmail.com
2Department of Statistics, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia. e-mail: ewencheko@yahoo.com
AMS subject classification:
The unbiased estimator of a p-variate population mean μ, the sample mean vector x¯, has traditionally been overemphasized, regardless of sample size. In this paper, alternative estimators of the parametric mean vector μ are developed. These estimators are biased and have lower mean-squared error (MSE) values. The properties of these estimators (in comparison to x¯) are explored.
Mean-squared error, more concentrated estimator, Pitman closer estimator, sample mean vector, sample variance-covariance matrix, relative efficiency
