2010Applied mathematical sciencesRequires access

Estimating the population mean using stratified median ranked set sampling

Kamarulzaman Ibrahim, Muhammed I. Syam, Amer Ibrahim Al‐Omari

Open publisher page 17 citations

Abstract

In this paper, stratified median ranked set sampling (SMRSS) method is suggested for estimating the population mean. The SMRSS is compared with simple random sampling (SRS), stratified simple random sampling (SSRS) and stratified ranked set sampling (SRSS). It is shown that SMRSS estimator is an unbiased of the population mean of symmetric distributions and is more efficient than its counterparts using SRS, SSRS and SRSS.

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What this paper is about

In this paper, stratified median ranked set sampling (SMRSS) method is suggested for estimating the population mean. The SMRSS is compared with simple random sampling (SRS), stratified simple random sampling (SSRS) and stratified ranked set sampling (SRSS). It is shown that SMRSS estimator is an unbiased of the population mean of symmetric distributions and is more efficient than its counterparts using SRS, SSRS and SRSS.

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OpenAlex reports 17 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this paper, stratified median ranked set sampling (SMRSS) method is suggested for estimating the population mean. The SMRSS is compared with simple random sampling (SRS), stratified simple random sampling (SSRS) and stratified ranked set sampling (SRSS). It is shown that SMRSS estimator is an unbiased of the population mean of symmetric distributions and is more efficient than its counterparts using SRS, SSRS and SRSS.

Key concepts: Stratified sampling, Simple random sample, Statistics, Mathematics, Sampling (signal processing), Estimator, Population mean, Sampling design

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