New Scheme of Empirical Likelihood Method for Ranked Set Sampling: Applications to Two One-Sample Problems

被引：0

作者：

Ahn, Soohyun ^{[1
]}

Wang, Xinlei ^{[2
]}

Moon, Chul ^{[3
]}

Lim, Johan ^{[4
]}

机构：

[1] Ajou Univ, Dept Math, Suwon, South Korea

[2] Univ Texas Arlington, Ctr Data Sci Res & Educ, Dept Math, Arlington, TX USA

[3] Southern Methodist Univ, Dept Stat & Data Sci, Dallas, TX USA

[4] Seoul Natl Univ, Dept Stat, Seoul, South Korea

来源：

INTERNATIONAL STATISTICAL REVIEW | 2024年

基金：

新加坡国家研究基金会;

关键词：

balanced ranked set sampling; empirical likelihood; jackknife empirical likelihood one sample test; unbalanced ranked set sampling; QUANTILES; ESTIMATOR; INFERENCE;

D O I：

10.1111/insr.12589

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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 1/H$$ 1/H $$, where H$$ H $$ 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.

引用

页数：40

共 34 条

[1] The Student's t approximation to distributions of pivotal statistics from ranked set samples [J].