Most of the research work on ratio, product, and regression estimators are usually based on conventional measures such as mean, quartiles, semi-interquartile range, semi-interquartile average, coefficient of skewness, coefficient of kurtosis, etc. The efficiency of these conventional measures is doubtful in the presence of extreme values in the data. In this paper, we propose an enhanced family of estimators for estimating the population variance using unconventional location measures such as tri-mean, Hodges-Lehmann, and decile mean of an auxiliary variable. The performance of the proposed family of estimators is compared with the existing estimators using a simulation study and two real populations. Also, the robustness of the proposed estimators was examined using an environment protection data with extreme values. The results showed that the proposed family performs better than its competitors not only in simple conditions but is also robust in the presence of extreme values.

中文翻译：

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使用辅助变量的非常规测度增强总体方差估计

大多数关于比率、乘积和回归估计量的研究工作通常基于常规度量，例如均值、四分位数、半四分位数极差、半四分位数平均值、偏度系数、峰度系数等。这些常规度量的效率在数据中存在极值的情况下，措施是值得怀疑的。在本文中，我们提出了一个增强型估计器系列，用于使用非常规位置度量（例如辅助变量的三均值、Hodges-Lehmann 和十分位数均值）来估计总体方差。使用模拟研究和两个真实群体将提议的估计器系列的性能与现有估计器进行比较。此外，还使用具有极值的环境保护数据检查了建议估计量的稳健性。