New efficient logarithmic estimators using multi-auxiliary information under ranked set sampling

This article proposes a new efficient logarithmic class of estimators for estimating the population mean in the case of multi-auxiliary information using ranked set sampling. The bias and mean square error of the proposed estimators are reported up to the first order of approximation. It is demonstrated analytically and numerically that the proposed estimators are always better than their competitors. We accomplished an empirical study using some real populations and a simulation study using some artificially generated populations to support the analytical comparisons between the proposed estimators and their competitors.