Large sample properties of maximum likelihood estimator using moving extremes ranked set sampling

In this paper, we investigate the maximum likelihood estimator (MLE) for the parameter θ in the probability density function f ( x ; θ ) . We specifically focus on the application of moving extremes ranked set sampling (MERSS) and analyze its properties in large samples. We establish the existence a...

Full description

Saved in:
Bibliographic Details
Published in:Journal of the Korean Statistical Society 2024, 53(2), , pp.398-415
Main Authors: Wang, Han, Chen, Wangxue, Li, Bingjie
Format: Article
Language:English
Subjects:
Applied Statistics
Bayesian Inference
Mathematics and Statistics
Research Article
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
통계학
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we investigate the maximum likelihood estimator (MLE) for the parameter θ in the probability density function f ( x ; θ ) . We specifically focus on the application of moving extremes ranked set sampling (MERSS) and analyze its properties in large samples. We establish the existence and uniqueness of the MLE for two common distributions when utilizing MERSS. Our theoretical analysis demonstrates that the MLE obtained through MERSS is, at the very least, as efficient as the MLE obtained through simple random sampling with an equivalent sample size. To substantiate these theoretical findings, we conduct numerical experiments. Furthermore, we explore the implications of imperfect ranking and provide a practical illustration by applying our approach to a real dataset.
ISSN:1226-3192
2005-2863
DOI:10.1007/s42952-023-00251-2