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On the Ranked-Set Sampling M-Estimates for Symmetric Location Families

机译:关于对称位置族的秩集抽样M估计

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摘要

The ranked-set sampling (RSS) is applicable in practical problems where the variable of interest for an observed item is costly or time-consuming but the ranking of a set of items according to the variable can be easily done without actual measurement. In this article, the M-estimates of location parameters using RSS data are studied. We deal mainly with symmetric location families. The asymptotic properties of M-estimates based on ranked-set samples are established. The properties of unbalanced ranked-set sample M-estimates are employed to develop the methodology for determining optimal ranked-set sampling schemes. The asymptotic relative efficiencies of ranked-set sample M-estimates are investigated. Some simulation studies are reported.
机译:排序集抽样(RSS)适用于实际问题,在这些问题中,所观察项目的目标变量成本高昂或耗时,但无需实际测量即可轻松完成根据变量对一组项目进行排名。在本文中,研究了使用RSS数据的位置参数的M估计。我们主要处理对称位置的家庭。建立了基于排序集样本的M估计的渐近性质。不平衡的排序集样本M估计值的属性用于开发确定最佳排序集采样方案的方法。研究了排序集样本M估计的渐近相对效率。报告了一些仿真研究。

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