On Efficient Estimation of the Population Mean under Stratified Ranked Set Sampling

Abstract

This paper considers some efficient combined and separate classes of estimators of the population mean in the presence of bivariate auxiliary information under stratified ranked set sampling. The mean square error MSE expressions of the proffered combined and separate classes of estimators are derived to the first order of approximation. The theoretical conditions are obtained under which the proffered combined and separate classes of estimators perform better than the existing combined and separate class of estimators. Subsequently, numerical and simulation studies are performed using real and artificially generated populations. The numerical and simulation results are found to be rewarding, showing the superiority of the proffered estimators over the existing estimators.

Improved estimation of population distribution function using twofold auxiliary information under simple random sampling

“…discussed some new families of estimators. The [ 10 ] proposed simple random sampling with a predictive estimator of the logarithmic type [ 11 ]. estimating population mean with a Stratified Ranked Sample [ 12 ].…”

Some Optimal Classes of Estimators Based on Multi-Auxiliary Information

“…Ref. [40] suggested some efficient population mean estimation methods under stratified RSS consisting of BAI.…”

On Cox proportional hazards model performance under different sampling schemes

“…Also, recently Jabrah et al [5] used RSS in a study involving human subjects, and they selected college students to analyze a psychological intervention to buttress resilience study. More applications of RSS and variations of RSS and their efficiency for estimation population means and other parameters are presents recently by several authors such as [6][7][8][9][10][11][12][13][14] among others.…”