Forthcoming

The Effects of Ranking Error Models on Mean Estimators Based on Ranked Set Sampling

Accepted - December 2021

Authors

Keywords:

ranked set sampling, ranking error models, relative efficiency, mean estimator, abalone dataset

Abstract

Ranked Set Sampling (RSS) is a sampling method commonly used in recent years. This sampling method is especially useful for studies in medicine, agriculture, forestry and ecology. In this study, the widely used ranking error models in RSS literature are investigated. This study is aimed to explore the effects of ranking error models on the mean estimators based on RSS and some of its modified methods such as Extreme RSS (ERSS) and Percentile RSS (PRSS) for different distribution, set and cycle size in infinite population. Monte Carlo simulation study is conducted for this purpose. Additionally, the study is supported by real life data. It is observed that, RSS and some of its modified methods shows better results than Simple Random Sampling (SRS).

Published

2021-12-13

How to Cite

Akdeniz , S., & Ozkal Yildiz , T. (2021). The Effects of Ranking Error Models on Mean Estimators Based on Ranked Set Sampling: Accepted - December 2021. REVSTAT-Statistical Journal. Retrieved from https://revstat.ine.pt/index.php/REVSTAT/article/view/406

Issue

Section

Forthcoming Paper

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