Estimation of Ratio Using Rank Set Sampling

Samawi, Hani M.; Muttlak, Hassen A.

doi:10.1002/bimj.4710380616

Cited by 112 publications

(78 citation statements)

References 10 publications

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“…2) DERSS is an extension to ERSS procedure by Samawi (2001). The procedure is just similar to that for DRSS, but taking ERSS instead of RSS in the first and in the second stage.…”

Section: Employ Again the Rss Procedures Inmentioning

confidence: 99%

“…Also, the ratio estimation method is used to obtain increased precision of estimating the population mean or total by taking advantage of the correlation between an auxiliary variable X and the variable of interest Y. In this paper, we assume that the Ratio Estimator Using RSS Samawi and Muttlak (1996) showed that the ratio estimator using RSS when ranking is on the variable X, is …”

Section: Ratio Estimatorsmentioning

confidence: 99%

“…Also assume ranking is on variable X. Let be the bivariate DERSS (see Samawi, 2001). This set of bivariate observations is independent but not identically distributed.…”

Section: Ratio Estimation Using Derssmentioning

confidence: 99%

“…i We can repeat the whole procedure m times to get a RSS of size n = mr. (See Takahasi and Wakimoto, 1968. ) 2) Similarly, as in Samawi et al (1996), we have two cases: In case of r is even, and if only RSS, 2) The sample, …”

Section: Introductionmentioning

confidence: 98%

“…Samawi and2001) used RSS and MRSS to improve the performance of the ratio estimator. Also, Samawi (2001) suggested the double extreme ranked set sampling (DERSS). They showed that ERSS, MRSS and DERSS are more practical than RSS and more efficient at least than SRS for estimating the population mean.…”

Section: Introductionmentioning

confidence: 99%

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Vol. 1, No. 2 (Full Issue)

2002

J. Mod. App. Stat. Meth.

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“…2) DERSS is an extension to ERSS procedure by Samawi (2001). The procedure is just similar to that for DRSS, but taking ERSS instead of RSS in the first and in the second stage.…”

Section: Employ Again the Rss Procedures Inmentioning

confidence: 99%

Section: Ratio Estimatorsmentioning

confidence: 99%

“…Also assume ranking is on variable X. Let be the bivariate DERSS (see Samawi, 2001). This set of bivariate observations is independent but not identically distributed.…”

Section: Ratio Estimation Using Derssmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Vol. 1, No. 2 (Full Issue)

2002

J. Mod. App. Stat. Meth.

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Improved nonparametric estimation of the optimal diagnostic cut‐off point associated with the Youden index under different sampling schemes

2016

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A diagnostic cut-off point of a biomarker measurement is needed for classifying a random subject to be either diseased or healthy. However, the cut-off point is usually unknown and needs to be estimated by some optimization criteria. One important criterion is the Youden index, which has been widely adopted in practice. The Youden index, which is defined as the maximum of (sensitivity + specificity -1), directly measures the largest total diagnostic accuracy a biomarker can achieve. Therefore, it is desirable to estimate the optimal cut-off point associated with the Youden index. Sometimes, taking the actual measurements of a biomarker is very difficult and expensive, while ranking them without the actual measurement can be relatively easy. In such cases, ranked set sampling can give more precise estimation than simple random sampling, as ranked set samples are more likely to span the full range of the population. In this study, kernel density estimation is utilized to numerically solve for an estimate of the optimal cut-off point. The asymptotic distributions of the kernel estimators based on two sampling schemes are derived analytically and we prove that the estimators based on ranked set sampling are relatively more efficient than that of simple random sampling and both estimators are asymptotically unbiased. Furthermore, the asymptotic confidence intervals are derived. Intensive simulations are carried out to compare the proposed method using ranked set sampling with simple random sampling, with the proposed method outperforming simple random sampling in all cases. A real data set is analyzed for illustrating the proposed method.

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Computing the effect of measurement errors on ranked set sampling estimators of the population mean

Vishwakarma

Singh

2022

Concurrency and Computation

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Summary The effect of measurement errors shows the seriousness of the estimators or the estimation procedures and cannot be avoided in sensitive cases because “a miss is as good as a mile.” In this article, we have proposed the very first ranked set estimators like unbiased, ratio, product, difference, ratio exponential, and product exponential estimators under both correlated and uncorrelated measurement errors. The expressions for bias and mean square error of the proposed estimators are obtained up to the first order of approximation. The proposed ranked set estimators have been compared through relative efficiencies over unbiased estimators and the effect of measurement errors has been given through percentage computation of measurement errors. The theoretical conditions are obtained under which the proposed estimator has performed better. The performance of the proposed ranked set estimators under measurement errors are shown by efficiency comparisons, Monte‐Carlo simulation study, and real data study.

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Estimation of Ratio Using Rank Set Sampling

Cited by 112 publications

References 10 publications

Vol. 1, No. 2 (Full Issue)

Vol. 1, No. 2 (Full Issue)

Improved nonparametric estimation of the optimal diagnostic cut‐off point associated with the Youden index under different sampling schemes

Computing the effect of measurement errors on ranked set sampling estimators of the population mean

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