Generalized Estimators of Population Mean in Stratified Ranked Set Sampling

by V.L.Mandowara and Nitu Mehta (Ranka) .

Abstract: 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.

Key Words: Stratified ranked set sampling, Ratio Estimator, Power transformation estimator, Auxiliary variable, Coefficient of variation, Coefficient of kurtosis

Nitu Mehta (Ranka),

Editor: Amjad D. Al-Nasser,

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