New Exponential Ratio Estimator in Ranked Set Sampling

Abstract

In this study, we adapted the families of estimators from Ünal and Kadilar (2021) using the exponential function for the population mean in case of non-response for simple random sampling for the estimation of the mean of the population with the RSS (ranked set sampling) method. The equations for the MSE and the bias of the adapted estimators are obtained for RSS and it in theory shows that the proposed estimator is additional efficient than the present RSS mean estimators in the literature. In addition, we support these theoretical results with real COVID-19 real data and conjointly the simulation studies with different distributions and parameters. As a result of the study, it was observed that the efficiency of the proposed estimator was better than the other estimators.

A novel proposed class of estimators under ranked set sampling: Simulation and diverse applications

“…[8] took inspiration from [30] and suggested a RSS based generalized class of estimators as where Δ is a constant and p is a real constant to develop numerous estimators. The optimum MSE of the estimator at is presented as Following [31] , [9] suggested a RSS based exponential estimator as follows where ϕ is an optimizing scalar to be used to minimize the MSE expression. The optimum MSE of the estimator at is presented as …”

“…The important simulation findings are given in the following points: The simulation outcomes of Table 2 obtained using the normal population demonstrate that the members , ,..., of the proposed estimators T attain the lesser MSE and higher PRE for each value of and perform better than the competing estimators such as unbiased estimator , traditional ratio estimator , traditional regression estimator , [4] estimator , [6] estimator , ,..., , [7] estimators , , , [8] estimator and [9] estimator . The simulation outcomes of Table 3 obtained using the exponential population demonstrate that the members , ,..., of the proposed estimators T attain the lesser MSE and higher PRE for each value of …”

“…The simulation outcomes of Table 3 obtained using the exponential population demonstrate that the members , ,..., of the proposed estimators T attain the lesser MSE and higher PRE for each value of and perform better than the competing estimators such as unbiased estimator , traditional ratio estimator , traditional regression estimator , [4] estimator , [6] estimators , ,..., , [7] estimators , , , [8] estimator and [9] estimator .…”

A Novel Sub-Type Mean Estimator for Ranked Set Sampling with Dual Auxiliary Variables

“…However, once the sample selection is finalized, the estimation process exclusively pertains to the study variable. While it is feasible to incorporate the auxiliary variable into the estimation phase through various estimator types (e.g., ratio, product, or regression type estimators using auxiliary variables) [4][5][6][7][8][9][10][11][12][13], such approaches often necessitate knowledge of population parameters. The undeniable impact of utilizing auxiliary variable information in enhancing efficiency is well established.…”