Publicação

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

Detalhes bibliográficos
Resumo:	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).
Autores principais:	Akdeniz , Sami
Outros Autores:	Ozkal Yildiz , Tugba
Assunto:	ranked set sampling ranking error models relative efficiency mean estimator abalone dataset
Ano:	2023
País:	Portugal
Tipo de documento:	artigo
Tipo de acesso:	unknown
Instituição associada:	Instituto Nacional de Estatística
Idioma:	inglês
Origem:	REVSTAT-Statistical Journal

Descrição
Resumo:	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).