Maximum likelihood estimation (MLE) applied to ranked set sampling (RSS) designs is usually based on the assumption of perfect ranking. However, it may suffers of lack of efficiency when ranking errors are present. The main goal of this article is to investigate the performance of six alternative estimation methods to MLE for parameter estimation under RSS. We carry out an extensive simulation study and measure the performance of the maximum product of spacings, ordinary and weighted least-squares, Cramér-von-Mises, Anderson–Darling and right-tail Anderson–Darling estimators, along with the maximum likelihood estimators, through the Kullback–Leibler divergence from the true and estimated probability density functions. Our simulation study considered eight continuous probability distributions, six sample sizes and six levels of correlation between the interest and concomitant variables. In general, our results show that the Anderson–Darling method outperforms its competitors and that the maximum likelihood estimators strongly depends on perfect ranking for accurate estimation. Finally, we present an illustrative example using a data set concerning the percent of body fat. R code is available in the supplementary material.

应用于排名集抽样（RSS）设计的最大似然估计（MLE）通常基于完美排名的假设。但是，当出现排名错误时，可能会缺乏效率。本文的主要目的是研究在RSS下用于参数估计的MLE的六种替代估计方法的性能。我们进行了广泛的模拟研究，并测量了最大间距，普通和加权最小二乘，Cramér-von-Mises，Anderson-Darling和右尾Anderson-Darling估计量以及最大似然估计量的性能，通过Kullback-Leibler与真实和估计的概率密度函数的差异。我们的模拟研究考虑了八种连续概率分布，六个样本大小以及相关变量和伴随变量之间的六个相关级别。总体而言，我们的结果表明，安德森–达林方法胜过其竞争对手，并且最大似然估计器强烈依赖于完美排名来进行准确的估计。最后，我们使用有关身体脂肪百分比的数据集来提供一个说明性示例。补充材料中提供了R代码。