Estimators for the finite population mean of the research variable are proposed in this article, employing ratio, product, and regression type estimators, all of which need just one auxiliary variable. A first-order approximation is developed for the mean squared errors of the techniques provided. It has been proven theoretically that the suggested estimators perform better than current estimators, and these theoretical conditions have been validated numerically using four data sets.
Citation: Anum Iftikhar, Hongbo Shi, Saddam Hussain, Ather Qayyum, M. El-Morshedy, Sanaa Al-Marzouki. Estimation of finite population mean in presence of maximum and minimum values under systematic sampling scheme[J]. AIMS Mathematics, 2022, 7(6): 9825-9834. doi: 10.3934/math.2022547
Estimators for the finite population mean of the research variable are proposed in this article, employing ratio, product, and regression type estimators, all of which need just one auxiliary variable. A first-order approximation is developed for the mean squared errors of the techniques provided. It has been proven theoretically that the suggested estimators perform better than current estimators, and these theoretical conditions have been validated numerically using four data sets.
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Anum Iftikhar, Hongbo Shi, Saddam Hussain, Ather Qayyum, M. El-Morshedy, Sanaa Al-Marzouki. Estimation of finite population mean in presence of maximum and minimum values under systematic sampling scheme[J]. AIMS Mathematics, 2022, 7(6): 9825-9834. doi: 10.3934/math.2022547
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