Ratio estimator for the population mean using ranked set sampling

Kadılar, Cem; Unyazici, Yesim; Çıngı, Hülya

doi:10.1007/s00362-007-0079-y

Cited by 58 publications

(27 citation statements)

References 5 publications

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“…Since, for this data y does not always increase with x, we have compared performances with the proposed five estimatorŝ µ y<rss> ,t <0> ,t <1> ,t <R> andt <d> based on judgement order statistic. However, as per suggestions from one of the referees, we have considered the following ratio estimator (Kadilar et al, 2009) and regression estimators when the population mean µ x is known…”

Section: Simulation Studiesmentioning

confidence: 99%

“…Stokes (1977) considered ranking as an auxiliary variable. Prasad (1989), Kadilar et al (2009) and Singh et al (2014) used the estimation of the population mean µ y assuming the population mean µ x is known. In our present paper we have proposed improved methods of estimation of the population mean using the ranking variable as an auxiliary variable when the population mean µ x is unknown.…”

Section: Introductionmentioning

confidence: 99%

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Improved Estimation from Ranked Set Sampling

Olaomi

Arnab

2014

HJMS

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Ranked set sampling is used when the measurement or quantification of units of the variable under study is difficult but the ranking of units of sets of small sizes can be done easily by an inexpensive method. Dell and Clutter (1972) showed that the sample mean based on ranked set sample is more efficient than the sample mean based on simple random sample with replacement sampling procedure for estimation of the population mean. In this paper Dell and Clutter estimator has been improved further by using the ranking variable x as an auxiliary variable when µx, the population mean of x is unknown. An empirical investigation based on life data shows all proposed estimators are approximately unbiased and bring gain in efficiency of up to 50 percent.

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Section: Simulation Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Estimation from Ranked Set Sampling

Olaomi

Arnab

2014

HJMS

View full text Add to dashboard Cite

show abstract

“…, m. In RSS based on a concomitant variable the supplementary information is used to obtain a judgmental RSS data. However, as discussed by Samawi and Muttlak (1996), as well as Kadilar et al (2009), the supplementary information can also be used at the estimation stage by using ratio estimators. To this end, Samawi and Muttlak (1996) proposed a ratio estimator of the population mean μ Y based on a ranked set sample of size n = mk as followsȲ…”

Section: Introductionmentioning

confidence: 99%

Unbiased and almost unbiased ratio estimators of the population mean in ranked set sampling

2011

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In this paper we study the problem of reducing the bias of the ratio estimator of the population mean in a ranked set sampling (RSS) design. We first propose a jackknifed RSS-ratio estimator and then introduce a class of almost unbiased RSS-ratio estimators of the population mean. We also present an unbiased RSS-ratio estimator of the mean using the idea of Hartley and Ross (Nature 174:270-271, 1954) which performs better than its counterpart with simple random sample data. We show that under certain conditions the proposed unbiased and almost unbiased RSS-ratio estimators perform better than the commonly used (biased) RSS-ratio estimator in estimating the population mean in terms of the mean square error. The theoretical results are augmented by a simulation study using a wheat yield data set from the Iranian Ministry of Agriculture to demonstrate the practical benefits of our proposed ratio-type estimators relative to the RSS-ratio estimator in reducing the bias in estimating the average wheat production.

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“…McIntyre [8] introduced the concept of RSS. Many authors such as Samawi and Muttlak [12], Bouza [1], Kadilar et al [5] and Mehta and Mandowara [9] use judgmental RSS where ranking is done with respect to auxiliary variable X. Taking motivation from these we shall adapt Koyuncu and Kadilar [7] family of estimators under RSS.…”

Section: Introductionmentioning

confidence: 99%

A regression type estimator for mean estimation under ranked set sampling alongside the sensitivity issue

Shahzad¹,

Hanif²,

Koyuncu³

et al. 2019

Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics

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Koyuncu and Kadilar [7] introduced a family of estimators under simple random sampling. In this article; we adapt these estimators for ranked set sampling. Further, we suggest a regression-type estimator of population mean utilizing available supplementary information under ranked set sampling scheme alongside the sensitivity issue when the variate of interest is sensitive. The bias and mean square error of the suggested estimator is determined theoretically for both situations. A simulation study has been done to demonstrate the percentage relative e¢ ciency of proposed estimators over the adapted and reviewed estimators.

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Ratio estimator for the population mean using ranked set sampling

Abstract: Ratio estimator, Ranked set sampling, Simple random sampling, Efficiency, 62D05,

Cited by 58 publications

References 5 publications

Improved Estimation from Ranked Set Sampling

Improved Estimation from Ranked Set Sampling

Unbiased and almost unbiased ratio estimators of the population mean in ranked set sampling

A regression type estimator for mean estimation under ranked set sampling alongside the sensitivity issue

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