Abstract:
Ranked Set Sampling (RSS) was initially suggested to increase the e ciency of the population
mean. It has been shown that this method is highly bene cial to the estimation based on simple random
sampling (SRS). The problem of estimating the population mean is an integral aspect of scienti c survey.
More e cient and less biased estimators in the form of regression estimator with minimum mean square
errors were obtained for population means. The estimator was examined for both cum-dual product and
cum-dual ratio under ranked set sampling without replacement. Expressions for the mean, mean square
error (MSE), bias and variance of the proposed estimators were derived to rst order of approximation.
The MSE of the newly proposed estimator for the product case was better for the correlation coe cient in
the range [-0.1 to 0.2] while for the ratio case, the MSE was more e cient for the correlation coe cient
in the range [-0.1 to 0.3] when compared to other existing estimators using monte carlo simulation. These
results indicate that the proposed new class of regression estimator for the population mean using ranked
set sampling is more e cient when compared to estimators based on simple random sampling (SRS) and
some existing estimators based on RSS.
