Stratified Dual-Rank Ranked Set for Estimating Population Mean

In surveys when measuring units is expensive, ranked set sampling (RSS) is a popular and economical sampling technique. The RSS algorithm selected units using a ranking procedure. Either eye inspection or an auxiliary variable is used for ranking. In the present paper, ‘stratified dual-rank ranked set sampling’ (SDuRSS) method is suggested to estimate population mean. The proposed design used dual ranking instead of traditional ranking method. The mean and variance of the suggested scheme is derived. The performance of the mean estimator of proposed scheme is investigated by relative efficiency (RE) of the estimator. A simulation study is conducted for computing such relative efficiency which shows that the proposed design is more efficient than stratified ranked set sampling (SRSS) and stratified extreme ranked set sampling (SERSS). The proposed scheme is illustrated with real data set where it also shows superiority on the SRSS and SERSS.