Large sample properties of modified maximum likelihood estimator of the location parameter using moving extremes ranked set sampling

IF 0.9 4区数学 Q3 STATISTICS & PROBABILITY

Statistics & Probability Letters Pub Date : 2025-04-11 DOI:10.1016/j.spl.2025.110430

Han Wang, Wangxue Chen

引用次数: 0

Abstract

The maximum likelihood estimator (MLE) obtained using moving extremes ranked set sampling (MERSS) typically does not have a closed form expression. In this study, we investigate a modified MLE (MMLE) utilizing MERSS for estimating the location parameter of a location family and analyze its properties in large samples. We derive the explicit form of the MMLE for two common distributions when MERSS is employed. The numerical results from two usual distributions indicate that the MMLE using MERSS is more efficient than that the MLE using simple random sampling with an equivalent sample size. The numerical results also indicate the loss of efficiency in using the MMLE under MERSS instead of the MLE under MERSS is very small for small values of

m

. Additionally, we examine the implications of imperfect ranking and demonstrate our approach using a real dataset.

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基于移动极值排序集抽样的位置参数修正极大似然估计的大样本性质

使用移动极值排序集抽样（MERSS）获得的最大似然估计量（MLE）通常不具有封闭形式表达式。在这项研究中，我们研究了一种改进的MLE (MMLE)，利用MERSS来估计位置族的位置参数，并分析了它在大样本中的性质。当采用MERSS时，我们推导了两种常见分布的MMLE的显式形式。两种常用分布的数值结果表明，使用MERSS的最大似然估计比使用相同样本容量的简单随机抽样的最大似然估计效率更高。数值结果还表明，对于较小的m值，使用MERSS下的MMLE而不是MERSS下的MLE的效率损失非常小。此外，我们检查了不完美排名的含义，并使用真实数据集演示了我们的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistics & Probability Letters 数学-统计学与概率论

CiteScore

1.60

自引率

0.00%

发文量

173

审稿时长

6 months

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