排序集抽样的经验似然法新方案:两个单样本问题的应用

IF 1.7 3区 数学 Q1 STATISTICS & PROBABILITY
Soohyun Ahn, Xinlei Wang, Chul Moon, Johan Lim
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引用次数: 0

摘要

我们提出了一种新的排序集抽样(RSS)经验似然法(EL),它充分利用了 RSS 的排序结构和信息。我们的新提案建议将每个等级层的层内概率之和限制为 ,其中为等级层的数量。附加约束的使用消除了非平衡 RSS 中主观权重选择的需要,并有助于将平衡 RSS 的方法无缝扩展到非平衡 RSS。我们将新建议应用于测试一个样本人群的平均值,并通过数值研究和两个真实世界的数据集来评估其性能,这两个数据集分别是通过体脂数据研究肥胖问题和通过人类牙齿大小数据研究牙齿大小的对称性。我们还进一步考虑了将提议的 EL 方法扩展到千斤顶 EL 的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Scheme of Empirical Likelihood Method for Ranked Set Sampling: Applications to Two One‐Sample Problems
We propose a novel empirical likelihood (EL) approach for ranked set sampling (RSS) that leverages the ranking structure and information of the RSS. Our new proposal suggests constraining the sum of the within‐stratum probabilities of each rank stratum to , where is the number of rank strata. The use of the additional constraints eliminates the need of subjective weight selection in unbalanced RSS and facilitates a seamless extension of the method for balanced RSS to unbalanced RSS. We apply our new proposal to testing one sample population mean and evaluate its performance through a numerical study and two real‐world data sets, examining obesity from body fat data and symmetry of dental size from human tooth size data. We further consider the extension of the proposed EL method to jackknife EL.
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来源期刊
International Statistical Review
International Statistical Review 数学-统计学与概率论
CiteScore
4.30
自引率
5.00%
发文量
52
审稿时长
>12 weeks
期刊介绍: International Statistical Review is the flagship journal of the International Statistical Institute (ISI) and of its family of Associations. It publishes papers of broad and general interest in statistics and probability. The term Review is to be interpreted broadly. The types of papers that are suitable for publication include (but are not limited to) the following: reviews/surveys of significant developments in theory, methodology, statistical computing and graphics, statistical education, and application areas; tutorials on important topics; expository papers on emerging areas of research or application; papers describing new developments and/or challenges in relevant areas; papers addressing foundational issues; papers on the history of statistics and probability; white papers on topics of importance to the profession or society; and historical assessment of seminal papers in the field and their impact.
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