Generalized Estimators of Population Mean in Stratified Ranked Set Sampling
by V.L.Mandowara and Nitu Mehta (Ranka) .
In the present investigation we have suggested two generalized estimators of population mean using power transformation based on Stratified Ranked set sampling (SRSS). Stratified Ranked Set Sampling combines the advantages of stratification and Ranked set sampling (RSS) to obtain an unbiased estimator for the population mean, with potentially significant gains in efficiency. It has been shown that these methods are highly beneficial to the estimation based on Stratified Simple Random Sampling (SSRS). The first order approximation to the bias and mean square error (MSE) of the proposed estimators are obtained. Theoretically, it is shown that these suggested estimators are more efficient than the estimators in Stratified simple random sampling. A numerical illustration is also carried out to demonstrate the merits of the proposed estimators using SRSS over the usual estimators in SSRS.
Stratified ranked set sampling, Ratio Estimator, Power transformation estimator, Auxiliary variable, Coefficient of variation, Coefficient of kurtosis
Nitu Mehta (Ranka), firstname.lastname@example.org
Amjad D. Al-Nasser, email@example.com
READING THE ARTICLE: You can read the article in
portable document (.pdf) format (229572 bytes.)
NOTE: The content of this article is the intellectual property of the authors, who retains all rights to future publication.
This page has been accessed 514 times since APRIL 21, 2016.
Return to the Home Page.