Optimal Imputation Methods under Stratified Ranked Set Sampling

Bibliographic Details
Main Author: Bhushan , Shashi
Publication Date: 2025
Other Authors: Kumar , Anoop
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: https://doi.org/10.57805/revstat.v23i1.501
Summary: It is long familiar that the stratified ranked set sampling (SRSS) is more efficient than ranked set sampling (RSS) and stratified random sampling (StRS). The existence of missing values alter the final inference of any study. This paper is fundamental effort to suggest some combined and separate imputation methods in presence of missing data under SRSS. It has been shown that the proposed imputation methods become superior than the mean imputation method, ratio imputation method, Diana and Perri (2010) type imputation method and Sohail et al. (2018) type imputation methods. A simulation study is administered over two hypothetically drawn asymmetric populations.
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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spelling Optimal Imputation Methods under Stratified Ranked Set Samplingmissing valuesimputationstratified ranked set samplingIt is long familiar that the stratified ranked set sampling (SRSS) is more efficient than ranked set sampling (RSS) and stratified random sampling (StRS). The existence of missing values alter the final inference of any study. This paper is fundamental effort to suggest some combined and separate imputation methods in presence of missing data under SRSS. It has been shown that the proposed imputation methods become superior than the mean imputation method, ratio imputation method, Diana and Perri (2010) type imputation method and Sohail et al. (2018) type imputation methods. A simulation study is administered over two hypothetically drawn asymmetric populations.Statistics Portugal2025-02-05info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.57805/revstat.v23i1.501https://doi.org/10.57805/revstat.v23i1.501REVSTAT-Statistical Journal; Vol. 23 No. 1 (2025): REVSTAT - Statistical Journal; 53-77REVSTAT; Vol. 23 N.º 1 (2025): REVSTAT - Statistical Journal; 53-772183-03711645-6726reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAPenghttps://revstat.ine.pt/index.php/REVSTAT/article/view/501https://revstat.ine.pt/index.php/REVSTAT/article/view/501/766https://revstat.ine.pt/index.php/REVSTAT/article/view/501/595Copyright (c) 2025 REVSTAT-Statistical Journalinfo:eu-repo/semantics/openAccessBhushan , ShashiKumar , Anoop2025-02-08T06:30:26Zoai:revstat:article/501Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:46:37.139544Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Optimal Imputation Methods under Stratified Ranked Set Sampling
title Optimal Imputation Methods under Stratified Ranked Set Sampling
spellingShingle Optimal Imputation Methods under Stratified Ranked Set Sampling
Bhushan , Shashi
missing values
imputation
stratified ranked set sampling
title_short Optimal Imputation Methods under Stratified Ranked Set Sampling
title_full Optimal Imputation Methods under Stratified Ranked Set Sampling
title_fullStr Optimal Imputation Methods under Stratified Ranked Set Sampling
title_full_unstemmed Optimal Imputation Methods under Stratified Ranked Set Sampling
title_sort Optimal Imputation Methods under Stratified Ranked Set Sampling
author Bhushan , Shashi
author_facet Bhushan , Shashi
Kumar , Anoop
author_role author
author2 Kumar , Anoop
author2_role author
dc.contributor.author.fl_str_mv Bhushan , Shashi
Kumar , Anoop
dc.subject.por.fl_str_mv missing values
imputation
stratified ranked set sampling
topic missing values
imputation
stratified ranked set sampling
description It is long familiar that the stratified ranked set sampling (SRSS) is more efficient than ranked set sampling (RSS) and stratified random sampling (StRS). The existence of missing values alter the final inference of any study. This paper is fundamental effort to suggest some combined and separate imputation methods in presence of missing data under SRSS. It has been shown that the proposed imputation methods become superior than the mean imputation method, ratio imputation method, Diana and Perri (2010) type imputation method and Sohail et al. (2018) type imputation methods. A simulation study is administered over two hypothetically drawn asymmetric populations.
publishDate 2025
dc.date.none.fl_str_mv 2025-02-05
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.v23i1.501
https://doi.org/10.57805/revstat.v23i1.501
url https://doi.org/10.57805/revstat.v23i1.501
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://revstat.ine.pt/index.php/REVSTAT/article/view/501
https://revstat.ine.pt/index.php/REVSTAT/article/view/501/766
https://revstat.ine.pt/index.php/REVSTAT/article/view/501/595
dc.rights.driver.fl_str_mv Copyright (c) 2025 REVSTAT-Statistical Journal
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2025 REVSTAT-Statistical Journal
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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. 23 No. 1 (2025): REVSTAT - Statistical Journal; 53-77
REVSTAT; Vol. 23 N.º 1 (2025): REVSTAT - Statistical Journal; 53-77
2183-0371
1645-6726
reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833598318458961920

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