Estimation of population mean using ranked set sampling in the presence of non-response error with numerical illustration and simulation study

Abstract This paper develops innovative estimators based on ranked set sampling (RSS) for estimating the population mean in the presence of non-response (NR) errors, utilizing auxiliary information. RSS is shown to be a more efficient alternative to simple random sampling (SRS), particularly under non-response conditions. The proposed estimators are evaluated against existing ratio, regression, and exponential estimators using bias, mean squared error (MSE), and percent relative efficiency (PRE). Results from empirical analysis and simulation studies demonstrate that the RSS-based estimators achieve lower MSE and higher PRE, thereby outperforming conventional methods. The study highlights the practical advantages of RSS in survey sampling and contributes to improving the robustness and accuracy of estimators under non-response error scenarios, while also suggesting directions for future research.