Efficiency of modified generalized imputation scheme for estimating population mean with known auxiliary information

A. Adejumobi, Ahmed Audu, M. A. Yunusa, R.V.K. Singh
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Abstract

Different authors for estimating population mean have proposed several Imputation schemes. Recently, some authors have suggested generalized imputation schemes that their estimators are functions of unknown parameters of the study variable. These unknown parameters need to be estimated for the estimators to be applicable and this may require additional resources. This paper considered a class of imputation scheme that is independent of unknown parameter and the point estimator of the suggested scheme for estimating population mean was derived. The properties (bias and MSE) of an efficient estimators presented were derived up to first order approximation and also conditions for which the estimators of the proposed scheme is more efficient than other estimators of the existing schemes considered in the study were also examined. The result of the empirical study revealed that the suggested estimators are more efficient than the existing ones considered in the study. 
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在已知辅助信息下估计总体均值的改进广义插值方法的有效性
不同的作者提出了几种估计总体均值的方法。最近,一些作者提出了广义归算方案,其估计量是研究变量的未知参数的函数。需要对这些未知参数进行估计,以使估算器适用,这可能需要额外的资源。本文考虑了一类不依赖于未知参数的估计方案,并给出了该方案的点估计量。给出了一阶近似下有效估计量的性质(偏置和均方误差),并对所提出方案的估计量比研究中考虑的其他现有方案的估计量更有效的条件进行了检验。实证研究结果表明,建议的估计量比研究中考虑的现有估计量更有效。
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