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

Detalhes bibliográficos
Autor(a) principal: Akdeniz , Sami
Data de Publicação: 2023
Outros Autores: Ozkal Yildiz , Tugba
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.57805/revstat.v21i3.406
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).
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spelling The Effects of Ranking Error Models on Mean Estimators Based on Ranked Set Samplingranked set samplingranking error modelsrelative efficiencymean estimatorabalone datasetRanked 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).Statistics Portugal2023-07-31info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.57805/revstat.v21i3.406https://doi.org/10.57805/revstat.v21i3.406REVSTAT-Statistical Journal; Vol. 21 No. 3 (2023): REVSTAT-Statistical Journal; 347–366REVSTAT; Vol. 21 N.º 3 (2023): REVSTAT-Statistical Journal; 347–3662183-03711645-6726reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttps://revstat.ine.pt/index.php/REVSTAT/article/view/406https://revstat.ine.pt/index.php/REVSTAT/article/view/406/650Copyright (c) 2021 REVSTAT-Statistical Journalinfo:eu-repo/semantics/openAccessAkdeniz , SamiOzkal Yildiz , Tugba2023-08-12T06:30:24Zoai:revstat:article/406Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:26:50.295660Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv The Effects of Ranking Error Models on Mean Estimators Based on Ranked Set Sampling
title The Effects of Ranking Error Models on Mean Estimators Based on Ranked Set Sampling
spellingShingle The Effects of Ranking Error Models on Mean Estimators Based on Ranked Set Sampling
Akdeniz , Sami
ranked set sampling
ranking error models
relative efficiency
mean estimator
abalone dataset
title_short The Effects of Ranking Error Models on Mean Estimators Based on Ranked Set Sampling
title_full The Effects of Ranking Error Models on Mean Estimators Based on Ranked Set Sampling
title_fullStr The Effects of Ranking Error Models on Mean Estimators Based on Ranked Set Sampling
title_full_unstemmed The Effects of Ranking Error Models on Mean Estimators Based on Ranked Set Sampling
title_sort The Effects of Ranking Error Models on Mean Estimators Based on Ranked Set Sampling
author Akdeniz , Sami
author_facet Akdeniz , Sami
Ozkal Yildiz , Tugba
author_role author
author2 Ozkal Yildiz , Tugba
author2_role author
dc.contributor.author.fl_str_mv Akdeniz , Sami
Ozkal Yildiz , Tugba
dc.subject.por.fl_str_mv ranked set sampling
ranking error models
relative efficiency
mean estimator
abalone dataset
topic ranked set sampling
ranking error models
relative efficiency
mean estimator
abalone dataset
description 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).
publishDate 2023
dc.date.none.fl_str_mv 2023-07-31
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.57805/revstat.v21i3.406
https://doi.org/10.57805/revstat.v21i3.406
url https://doi.org/10.57805/revstat.v21i3.406
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://revstat.ine.pt/index.php/REVSTAT/article/view/406
https://revstat.ine.pt/index.php/REVSTAT/article/view/406/650
dc.rights.driver.fl_str_mv Copyright (c) 2021 REVSTAT-Statistical Journal
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 REVSTAT-Statistical Journal
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Statistics Portugal
publisher.none.fl_str_mv Statistics Portugal
dc.source.none.fl_str_mv REVSTAT-Statistical Journal; Vol. 21 No. 3 (2023): REVSTAT-Statistical Journal; 347–366
REVSTAT; Vol. 21 N.º 3 (2023): REVSTAT-Statistical Journal; 347–366
2183-0371
1645-6726
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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