The New Sub-regression Type Estimator in Ranked Set Sampling

Koçyiğit, Eda Gizem; Rather, Khalid Ul Islam

doi:10.1007/s42519-023-00324-9

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Building upon the foundation laid by the sub-ratio, sub-exponential, and sub-regression type estimators [18,19], as well as the multiple auxiliary variable estimators [14][15][16][17], we introduce a novel sub-estimator for the population mean under ranked set sampling with two auxiliary variables with the following formulation:…”

Section: Proposed Estimatormentioning

confidence: 99%

A Novel Sub-Type Mean Estimator for Ranked Set Sampling with Dual Auxiliary Variables

KOÇYİĞİT

2023

Journal of New Theory

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This research introduces a novel sub-estimator designed to estimate the population mean under ranked set sampling, motivated by the new concept of a recently introduced sub-ratio estimator. The mathematical formulas of the proposed estimator’s mean square error and bias are presented and theoretically contrasted with an analogous estimator found in the existing best sub-estimator literature. In addition to the theoretical analysis, empirical evidence is provided to validate the superiority of the proposed estimator. This empirical validation is based on numerical computations using Monte Carlo simulations, encompassing synthetic and real data applications. The results underscore the effectiveness of the proposed estimator. Finally, this study discusses the need for further research.

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Section: Proposed Estimatormentioning

confidence: 99%

A Novel Sub-Type Mean Estimator for Ranked Set Sampling with Dual Auxiliary Variables

KOÇYİĞİT

2023

Journal of New Theory

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“…[19] investigated a novel class of regression cum ratio estimators of population mean based on RSS. Some comprehensive studies based on RSS were presented by [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Some Optimal Classes of Estimators Based on Multi-Auxiliary Information

Bhushan

Kumar

Alsadat

et al. 2023

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Ranked set sampling (RSS) has been proven an efficient alternative to simple random sampling (SRS). The use of auxiliary information also helps to improve the efficiency of the estimation procedures. Therefore, to accomplish higher efficiency and discuss the optimality issues, we proffer some optimal classes of estimators under RSS by employing multi-auxiliary information. It is seen that the ordinary mean estimator, traditional regression, and ratio estimators are the subsets of the proffered estimators. The expressions of the bias and mean square error are reported. An analytical comparison under some optimality conditions points out the ascendancy of the proffered classes of estimators over all reviewed works. The theoretical results have been furnished with computational study by employing some artificial and natural populations. The computational results show that the proffered estimators outperform the conventional estimators reviewed in this study. Furthermore, apposite advices are suggested to the survey persons.

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“…The work [10] introduced an innovative sub-regression estimator for ranked set sampling (RSS), substantiated by theoretical analysis, simulations, and real-world data studies. Results indicate the superior effectiveness of the proposed estimator compared to existing literature.…”

mentioning

confidence: 99%

A Reﬁnement of Ratio Estimation in Ranked Set Sampling and Stratiﬁed Ranked Set Sampling Approaches

Khan,

Javid shabbir,

Qazi Ubaid

et al. 2024

VFAST trans. math.

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Estimation of population mean is a persistent subject issue in sampling surveys and many discrete efforts have been paid by various researchers to enhance the precision of the estimates by utilizing the correlated auxiliary information. In connection with this an improved version of ratio estimator are presented in this paper under the ranked set sampling scheme and stratified ranked set sampling scheme. Comparison amongst estimators is made in terms of Mean Square Errors ( ) and Percentage Relative Efficiencies ( ). The expression for of the proposed estimator is pinned-down to first order of approximations. It turns out from both simulation studies as well as real data set that the proposed estimator dominates its existing counterpart estimators.

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The New Sub-regression Type Estimator in Ranked Set Sampling

Cited by 5 publications

References 12 publications

A Novel Sub-Type Mean Estimator for Ranked Set Sampling with Dual Auxiliary Variables

A Novel Sub-Type Mean Estimator for Ranked Set Sampling with Dual Auxiliary Variables

Some Optimal Classes of Estimators Based on Multi-Auxiliary Information

A Reﬁnement of Ratio Estimation in Ranked Set Sampling and Stratiﬁed Ranked Set Sampling Approaches

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