排序集抽样中新的子回归类型估计。

IF 0.6 Q4 STATISTICS & PROBABILITY

Journal of Statistical Theory and Practice Pub Date : 2023-01-01 DOI:10.1007/s42519-023-00324-9

Eda Gizem Koçyiğit, Khalid Ul Islam Rather

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引用次数: 2

摘要

在本研究中，基于Koçyiğit和Kadılar (common Stat Theory Methods 1- 23,2022)中给出的子比率估计器的思想，提出了一种新的排序集抽样(RSS)的子回归型估计器。得到了无偏估计量的均方误差，并与其他估计量进行了理论比较。理论结果得到了不同模拟和实际数据集研究的支持，并表明所提出的估计器比文献中的估计器更有效。还可以看出，RSS中的重复次数影响了子估计器的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

The New Sub-regression Type Estimator in Ranked Set Sampling.

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The New Sub-regression Type Estimator in Ranked Set Sampling.

In this study, a new sub-regression type estimator for ranked set sampling (RSS) is proposed based on the idea of a sub-ratio estimator given in Koçyiğit and Kadılar (Commun Stat Theory Methods 1-23, 2022). The proposed unbiased estimator's mean square error is obtained and compared theoretically with other estimators. The theoretical results have been supported by the different simulations and real-life data sets studies and have shown that the proposed estimator is more effective than the estimators in the literature. It is also seen that the number of repetitions in the RSS affected the effectiveness of the sub-estimators.

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来源期刊

Journal of Statistical Theory and Practice STATISTICS & PROBABILITY-

CiteScore

1.40

自引率

0.00%

发文量