On efficient median-based linear regression estimator for population mean

For the past decade, literatures in sampling theory show that estimation of mean is one of the challenging aspects which has received wide attention from research community with the aim of obtaining efficient estimators. In this research paper, we propose an efficient median-based linear regression estimator that utilizes both median of study variable and auxiliary variable at a time using Subramani and Lamichhane et al.'s methods of estimation. The bias and mean square error of the proposed estimators are derived up to first-order approximation alongside its efficiency condition. A real-life dataset and simulation study is used to compare the efficiency of the proposed estimator with some existing efficient estimators. Findings from the empirical and simulation analysis show a significant efficiency gain (more than 10%), hence the estimator is recommended for practical application.