New Scheme of Empirical Likelihood Method for Ranked Set Sampling: Applications to Two One‐Sample Problems
提出一种新的经验似然方法用于排序集抽样，通过添加约束消除权重选择问题，并应用于检验单样本总体均值，用体脂数据和牙齿尺寸数据验证了效果。
Summary 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.