Modified ratio estimators using stratified ranked set sampling

Year 2014, Volume: 43 Issue: 3, 461 - 471, 01.06.2014

V.l. Mandowara Nitu Mehta (ranka)

Abstract

Stratified Ranked Set Sampling (SRSS) 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. The present paper deals with modified ratio estimators
of finite population mean using information on coefficient of variation
and co-efficient of kurtosis of auxiliary variable in Stratified Ranked
Set Sampling. It has been shown that these methods are highly beneficial to the estimation based on Stratified Simple Random Sampling
(SSRS). The bias and mean squared error of the proposed estimators
with large sample approximation are derived. Theoretically, it is shown
that these suggested estimators are asymptotically more efficient than
the estimators in stratified simple random sampling. The results have
been illustrated by numerical example.

Keywords

Stratified ranked set sampling, Ratio-type estimators, Ranked set sampling, Auxiliary variables, Mean squared error, Population mean, Coefficient of variation, Coefficient of kurtosis, Efficiency

References

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