Improved generalized class of estimators in estimating the finite population mean using two auxiliary variables under two-stage sampling

Ahmad, Sohaib; Hussain, Sardar; Shabbir, Javid; Aamir, Muhammad; El-Morshedy, Mahmoud; Ahmad, Zubair; Alrajhi, Sharifah

doi:10.3934/math.2022592

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…The authors in Ref. [ 11 ] recommended a family of estimators for population proportion using idea of rank. The [ 12 ] was the first who placed his consistent efforts to introduce this method for efficient estimating the average pasture yield in forest.…”

Section: Introductionmentioning

confidence: 99%

Enhanced exponential ratio-cum-ratio estimator in ranked set sampling using transformed auxiliary information

Khan,

Ahmad,

Alomair

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Enhanced exponential ratio-cum-ratio estimator in ranked set sampling using transformed auxiliary information

Khan,

Ahmad,

Alomair

et al. 2024

Heliyon

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“…Cochran [7] derived a new ratio estimator for the mean using auxiliary information. Bhushan et al [6] and Ahmad et al [2] also proposed many efficient estimators with the help of different sampling techniques. Subramani and Kumarapandiyan [19] started work by developing an enhanced ratio estimator for the estimation of a population mean utilizing different parameters of the concomitant variable.…”

Section: Introductionmentioning

confidence: 99%

“…Zaman and Kadilar [23] developed a novel family of estimators using the information of supplementary characteristics. Ahmad et al [3] and Yunusa [4] developed a class of general type estimators for the parameters of a population using the information of auxiliary attributes. Saini et al [15] suggested optimum estimators under dual supplementary attributes with application in education, fisheries and agriculture areas.…”

Section: Introductionmentioning

confidence: 99%

Improved Mean Estimators for Population utilizing Dual Supplementary Characteristics under Simple Random Sampling

Hussain,

Zaman,

Khan

et al. 2024

VFAST trans. math.

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This paper makes another addition to the existing literature of population mean estimation. An improved family of mean estimators for the population is suggested using simple random sampling and utilizing the information of dual supplementary characteristics. We have conducted an extensive theoretical and numerical investigation of these recommended estimators using the criteria of bias and mean square error. The study provides a derivation of the bias and mean square error of the recommended estimators, approximating up to the first order and compared with the existing estimators. The suggested estimators are also extensively studied numerically and compared using real-world data sets and simulation studies. The theoretical and numerical comparisons show that the estimators suggested in the study are much more efficient than the competitor estimators under all situations i.e. Bahl and Tuteja [21] and other existing estimators in the literature. Therefore, the suggested family of estimators can be applied to real-life problems to obtain better results than the existing mean estimators for the population.

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“…Muhammad et al 13 suggested an enhanced ratio-type estimator for finite population mean using the auxiliary variable in simple random sampling. Ahmad et al 14 discussed an improved generalized class of estimators in estimating the finite population mean using two auxiliary variables under two-stage sampling. Shahzad et al 15 proposed a three-fold utilization of supplementary information for mean estimation under median-ranked set sampling scheme.…”

Section: Introductionmentioning

confidence: 99%

New generalized class of estimators for estimation of finite population mean based on probability proportional to size sampling using two auxiliary variables: A simulation study

Ahmad,

Shabbir,

Zahid

et al. 2023

Science Progress

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This article aims to suggest a new generalized class of estimators based on probability proportional to size sampling using two auxiliary variables. The numerical expressions for the bias and mean squared error (MSE) are derived up to the first order of approximation. Four actual data sets are used to examine the performances of a new improved generalized class of estimators. From the results of real data sets, it is examined that the suggested estimator gives the minimum MSE and the percentage relative efficiency is higher than all existing estimators, which shows the importance of the new generalized class of estimators. To check the strength and generalizability of our proposed class of estimators, a simulation study is also accompanied. The consequence of the simulation study shows the worth of newly found proposed class estimators. Overall, we get to the conclusion that the proposed estimator outperforms as compared to all other estimators taken into account in this study.

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Improved generalized class of estimators in estimating the finite population mean using two auxiliary variables under two-stage sampling

Cited by 9 publications

References 19 publications

Enhanced exponential ratio-cum-ratio estimator in ranked set sampling using transformed auxiliary information

Enhanced exponential ratio-cum-ratio estimator in ranked set sampling using transformed auxiliary information

Improved Mean Estimators for Population utilizing Dual Supplementary Characteristics under Simple Random Sampling

New generalized class of estimators for estimation of finite population mean based on probability proportional to size sampling using two auxiliary variables: A simulation study

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