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Estimation of the Population Mean Based on Extremes Ranked Set Sampling

机译:基于极限排序集抽样的总体均值估计

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This paper is concerned with ranked set sampling theory which is useful to estimate the population mean when the order of a sample of small size can be found without measurements or with other methods. In practice ranking a sample of moderate size and observing the i-th ranked unit (ranking of middle ordered units) is a difficult task. Therefore, in this paper we propose two estimators of the population mean based on extremes ranked set sampling methods. The proposed estimators are unbiased for the population mean when the underlying distribution is symmetric. It is shown that the proposed estimators are more efficient than their counter part simple random sampling method for distributions considered in this study.
机译:本文关注的是排序集抽样理论,该理论可用于在无需测量或其他方法的情况下找到小尺寸样本的顺序时估计总体均值。在实践中,对中等大小的样本进行排名并观察第i个排名单位(对中阶单位进行排名)是一项艰巨的任务。因此,在本文中,我们提出了基于极端排名集抽样方法的总体均值的两个估计量。当基础分布是对称的时,建议的估计量对于总体均值是无偏的。结果表明,对于本研究中考虑的分布,所提出的估计量比其对等部分的简单随机抽样方法更为有效。

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