On stratified ranked set sampling for the quest of an optimal class of estimators

In the sample survey theory, the crux of survey practitioners is to provide accurate estimators of the parameter of choice. The conventional theory depends on the regression/difference estimators as they correspond to the best linear unbiased (BLU) estimators. This paper suggests some optimal classes of estimators by modifying the conventional estimators under stratified ranked set sampling (SRSS). The characteristics of the suggested estimators are established to the first-order approximation. The performance of the suggested class of estimators under SRSS has been theoretically and experimentally shown to be superior to traditional estimators, particularly regression (BLU) estimators.

Automated summary

In the sample survey theory, accurate estimation of parameters is essential for survey practitioners. This paper suggests optimal classes of estimators by modifying conventional estimators under stratified ranked set sampling (SRSS). The suggested estimators have been shown to outperform traditional estimators, particularly regression (BLU) estimators, both theoretically and experimentally.