Stratified Unified Ranked Set Sampling for Asymmetric Distributions
Keywords:
simple random sampling, ranked set sampling, unified ranked set sampling, stratified unified ranked set samplingAbstract
The purposes of this study are to propose a modified ranked set sampling, which is called stratified unified ranked set sampling (SURSS), for estimating the population mean and compare the efficiency of the empirical mean estimator based on SURSS with their counterparts in simple random sampling (SRS), stratified simple random sampling ( SSRS) , and stratified ranked set sampling ( SRSS) via a simulation. We compare efficiency using criteria mean square error (MSE) and simulate the data from three asymmetric distributions: Exponential (1), Geometric (0.5), and Gamma (1, 2). It is found that the estimator in SURSS provides more efficient
than their counterparts in SRS, SSRS, and SRSS for three parent asymmetric distributions with small sample size. For the larger sample size, the proposed estimator in SURSS still provide more efficient than SRS, but it gives more efficient than SSRS and SRSS in some cases.
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