Multistage ranked set sampling

Al-Saleh, Mohammad Fraiwan; Al-Omari, Amer Ibrahim

doi:10.1016/s0378-3758(01)00086-6

Cited by 145 publications

(79 citation statements)

References 19 publications

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“…The values of (N, k) are selected to be (10, 5) , (10,10) , (20,5) , (20,10). So, we can assess the effect of increasing total sample size for fixed k, and the effect of increasing k when the total sample size is fixed.…”

Section: Monte Carlo Comparisonmentioning

confidence: 99%

“…Al-Saleh and Al-Omari [5], Al-Omari and Al-Saleh [3] and Al-Omari [1], [2] proposed some mean estimators in other variations of the RSS. Stokes [20] suggested an estimator of the population variance based on RSS and showed that it is asymptotically (n → ∞ or k → ∞) unbiased of the population variance and has greater efficiency than the sample variance using SRS regardless of the issue of ranking.…”

Section: Introductionmentioning

confidence: 99%

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New ranked set sampling for estimating the population mean and variance

Zamanzade¹

2015

HJMS

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The purpose of this study is to suggest a new modification of the usual ranked set sampling (RSS) method, namely; neoteric ranked set sampling (NRSS) for estimating the population mean and variance. The performance of the empirical mean and variance estimators based on NRSS are compared with their counterparts in ranked set sampling and simple random sampling (SRS) via Monte Carlo simulation. Simulation results revealed that the NRSS estimators perform much better than their counterparts using RSS and SRS designs when the ranking is perfect. When the ranking is imperfect, the NRSS estimators are still superior to their counterparts in ranked set sampling and simple random sampling methods. These findings show that the NRSS provides a uniform improvement over RSS without any additional costs. Finally, an illustrative example of a real data is provided to show the application of the new method in practice.

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Section: Monte Carlo Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New ranked set sampling for estimating the population mean and variance

Zamanzade¹

2015

HJMS

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“…Al-Saleh and Al-Kadiri (2000) introduced Double Ranked Set Sampling for estimating the population mean. Al-Saleh and Al-Omari (2002) suggested Multistage Ranked Set Sampling that increases the efficiency of estimating the population mean for specific value of the sample size. Jemain and Al-Omari (2006) suggested Multistage Median Ranked Set Sampling (MMRSS) to estimate a population mean.…”

Section: Introductionmentioning

confidence: 99%

On the Mean Estimation using Stratified Double Median Ranked Set Sampling

Syam¹,

Al-Omari²,

Ibrahim³

2017

American Journal of Applied Sciences

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Stratified Double Median Ranked Set Sampling (SDMRSS) method is suggested for estimating the population mean. The SDMRSS is compared with the Simple Random Sampling (SRS), Stratified Simple Random Sampling (SSRS) and Stratified Ranked Set Sampling (SRSS) methods. It is shown that SDMRSS estimator is an unbiased of the population mean and is more efficient than the SRS, SSRS and SRSS counterparts. Also, SDMRSS increase the efficiency of mean estimation for specific value of the sample size. The SDMRSS is applied on real data set.

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“…The RSS consists of these m selected units. For recent work, consult Patil et al (1999), Al-Saleh and Al-Kadiri (2000), Al-Saleh and Samawi (2000), Chen (2000), Samawi (2001), Zheng and Al-Saleh (2002) and Al-Saleh and Al-Omari (2002).…”

Section: Introductionmentioning

confidence: 99%

Estimation Using Bivariate Extreme Ranked Set Sampling With Application To The Bivariate Normal Distribution

Al-Saleh

Samawi

2004

J. Mod. App. Stat. Meth.

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In this article, the procedure of bivariate extreme ranked set sampling (BVERSS) is introduced and investigated as a procedure of obtaining more accurate samples for estimating the parameters of bivariate populations. This procedure takes its strength from the advantages of bivariate ranked set sampling (BVRSS) over the usual ranked set sampling in dealing with two characteristics simultaneously, and the advantages of extreme ranked set sampling (ERSS) over usual RSS in reducing the ranking errors and hence in being more applicable. The BVERSS procedure will be applied to the case of the parameters of the bivariate normal distributions. Illustration using real data is also provided.

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Multistage ranked set sampling

Cited by 145 publications

References 19 publications

New ranked set sampling for estimating the population mean and variance

New ranked set sampling for estimating the population mean and variance

On the Mean Estimation using Stratified Double Median Ranked Set Sampling

Estimation Using Bivariate Extreme Ranked Set Sampling With Application To The Bivariate Normal Distribution

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