Estimation of Population Variance from Multi-ranker Ranked Set Sampling Designs

Öztürk, Ömer; Demirel, Neslihan

doi:10.1080/03610918.2014.948191

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…For proper treatment, a doctor must have a detailed realization of differences in human hypertension and fever. Many authors suggested different types of estimators for variance including Das [1], Prasad and Singh [2], Swain and Mishra [3], Ahmad and Shabbir [4], Kadilar and Cingi [5], Subramani and Kumarapandiyan [6], Singh and Solanki [7], Yadav et al [8], Singh et al [9], Yadav and Kadilar [10], Adichwal et al [11,12], Yasmeen et al [13], Zaman [14,15], Zaman and Bulut [16], Zaman et al [17,18], Bhushan et al [19], Shahzad et al [20], Ahmad et al [21], Ozturk [22], Ozturk and Demirel [23], Frey and Feeman [24], Zamanzade and Vock [25], Mahdizadeh and Zamanzade [26,27], Bhushan et al [28], Bhushan et al [29], Zaman and Bulut [30,31], Yadav and Zaman [32], Zaman and Kadilar [33], Zaman and Dunder [34] and Singh and Khalid [35]. We requisite to estimate population parameters for numerous variables in multidisciplinary surveys, such as house lengths, income level, and the number of unemployed people, in socio-economic surveys.…”

Section: Introductionmentioning

confidence: 99%

A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling

et al. 2022

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In this article, we proposed an improved finite population variance estimator based on simple random sampling using dual auxiliary information. Mathematical expressions of the proposed and existing estimators are obtained up to the first order of approximation. Two real data sets are used to examine the performances of a new improved proposed estimator. A simulation study is also recognized to assess the robustness and generalizability of the proposed estimator. From the result of real data sets and simulation study, it is examining that the proposed estimator give minimum mean square error and percentage relative efficiency are higher than all existing counterparts, which shown the importance of new improved estimator. The theoretical and numerical result illustrated that the proposed variance estimator based on simple random sampling using dual auxiliary information has the best among all existing estimators.

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Section: Introductionmentioning

confidence: 99%

A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling

et al. 2022

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“…If the ranking cost is negligible, combining sets of ranks improves the information content of a JPS sample without impacting its sampling cost. In a slightly different context, Ozturk (2013) and Ozturk and Demirel (2016) used the tie structures among K rankers to construct a ranked set sample and estimate the population mean and variance. Stokes et al (2007) used the raking principle in contingency tables to incorporate ranking information into a JPS sample.…”

Section: Introductionmentioning

confidence: 99%

Judgment Post-stratified Assessment Combining Ranking Information from Multiple Sources, with a Field Phenotyping Example

Öztürk

Kravchuk

2021

JABES

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This paper presents novel estimators for a judgment post-stratified (JPS) sample, which combine the ranking information from different methods or rankers. A JPS sample divides the units in the original simple random sample (SRS) into several ranking groups based on the relative positions (ranks) of the units in their individual small comparison sets. Ranks in the comparison sets may be assigned with several different ranking procedures. When considered separately, each ranking method leads to a different JPS sample estimator of the population mean or total. Here we introduce equally or unequally weighted estimators, which combine the ranking information from multiple sources. The unequal weights utilize the standard errors of the individual ranking methods estimators. The weighted estimators provide a substantial improvement over an SRS estimator and a JPS estimator based on a single ranking method. The new estimators are applied to crop establishment phenotypic data from an agricultural field experiment.Supplementary materials accompanying this paper appear online.

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“…Ozturk (2013) used agreement scores of multiple observers to break the ties, to determine the units to be measured, and ultimately to construct a weighted mean estimator. Ozturk & Demirel (2016) used a similar idea to estimate the population variance using partially ordered RSS data. Cetintav et al (2017) considered a slightly different approach and used fuzzy sets to combine the ranking information from multiple ranking methods.…”

Section: Introductionmentioning

confidence: 99%

Combining ranking information from different sources in ranked‐set samples

Öztürk

Kravchuk

2021

Can J Statistics

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This article considers a ranked-set sample (RSS) with multiple ranking methods. The RSS data have K sets of ranks, each of which leads to a different estimator. We provide a weighted estimator that combines ranking information from these K sets of ranks. The weights are computed from the agreement scores of the ranking methods. We show that the new estimator provides substantial improvement over a balanced RSS estimator. The new estimator is applied to seed emergence data from a field experiment in agricultural research.

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Estimation of Population Variance from Multi-ranker Ranked Set Sampling Designs

Cited by 8 publications

References 17 publications

A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling

A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling

Judgment Post-stratified Assessment Combining Ranking Information from Multiple Sources, with a Field Phenotyping Example

Combining ranking information from different sources in ranked‐set samples

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