Abstract
Koyuncu and Kadilar <cite>kk.2009</cite> introduced a family of estimators under simple random sampling. In this article; we adapt these estimators for ranked set sampling. Further, we suggest a regression-type estimator of population mean utilizing available supplementary information under ranked set sampling scheme alongside the sensitivity issue when the variate of interest is sensitive. The bias and mean square error of the suggested estimator is determined theoretically for both situations. A simulation study has been done to demonstrate the percentage relative efficiency of proposed estimators over the adapted and reviewed estimators.
Highlights
In sampling survey, the supplementary information is mostly utilized to enhance precision of the estimators due to correlation between the study and the supplementary variables
Koyuncu and Kadilar [7] introduced a family of estimators under simple random sampling
In this article; we adapt these estimators for ranked set sampling
Summary
The supplementary information is mostly utilized to enhance precision of the estimators due to correlation between the study and the supplementary variables. Many authors including Sisodia and Dwivedi [17], Singh and Kakran [14], Upadhyaya and Singh [20], Tailor and Sharma [19], Koyuncu and Kadilar [7] and Shahzad [13] have developed some estimators for estimation of the population mean Y under simple random sampling (SRS) scheme. It is realized that the estimate of the population mean utilizing ranked set sampling (RSS) is more productive than the one acquired utilizing SRS. McIntyre [8] introduced the concept of RSS Many authors such as Samawi and Muttlak [12], Bouza [1], Kadilar et al [5] and Mehta and Mandowara [9] use judgmental RSS where ranking is done with respect to auxiliary variable X.
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