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An efficient class of estimators for finite population mean in the presence of non-response under ranked set sampling (RSS)

In this study, we address the problem of estimating the finite population mean when the non-response occurs on the characteristics under study. We propose a class of Rao-regression type estimators when ranked set sampling (RSS) procedure is used to collect the data from non-response group only and f...

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Detalles Bibliográficos
Autores principales: Rehman, Syed Abdul, Shabbir, Javid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9757593/
https://www.ncbi.nlm.nih.gov/pubmed/36525461
http://dx.doi.org/10.1371/journal.pone.0277232
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author Rehman, Syed Abdul
Shabbir, Javid
author_facet Rehman, Syed Abdul
Shabbir, Javid
author_sort Rehman, Syed Abdul
collection PubMed
description In this study, we address the problem of estimating the finite population mean when the non-response occurs on the characteristics under study. We propose a class of Rao-regression type estimators when ranked set sampling (RSS) procedure is used to collect the data from non-response group only and from both, the response and non-response groups. The information provided on the auxiliary variable is used at both stages i.e., at designing stage and the estimation stage. Expressions for bias and mean square error of the estimators are obtained up to first order of approximation. A comprehensive simulation study is carried out to observe the performances of the estimators under non-response.
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spelling pubmed-97575932022-12-17 An efficient class of estimators for finite population mean in the presence of non-response under ranked set sampling (RSS) Rehman, Syed Abdul Shabbir, Javid PLoS One Research Article In this study, we address the problem of estimating the finite population mean when the non-response occurs on the characteristics under study. We propose a class of Rao-regression type estimators when ranked set sampling (RSS) procedure is used to collect the data from non-response group only and from both, the response and non-response groups. The information provided on the auxiliary variable is used at both stages i.e., at designing stage and the estimation stage. Expressions for bias and mean square error of the estimators are obtained up to first order of approximation. A comprehensive simulation study is carried out to observe the performances of the estimators under non-response. Public Library of Science 2022-12-16 /pmc/articles/PMC9757593/ /pubmed/36525461 http://dx.doi.org/10.1371/journal.pone.0277232 Text en © 2022 Rehman, Shabbir https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Rehman, Syed Abdul
Shabbir, Javid
An efficient class of estimators for finite population mean in the presence of non-response under ranked set sampling (RSS)
title An efficient class of estimators for finite population mean in the presence of non-response under ranked set sampling (RSS)
title_full An efficient class of estimators for finite population mean in the presence of non-response under ranked set sampling (RSS)
title_fullStr An efficient class of estimators for finite population mean in the presence of non-response under ranked set sampling (RSS)
title_full_unstemmed An efficient class of estimators for finite population mean in the presence of non-response under ranked set sampling (RSS)
title_short An efficient class of estimators for finite population mean in the presence of non-response under ranked set sampling (RSS)
title_sort efficient class of estimators for finite population mean in the presence of non-response under ranked set sampling (rss)
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9757593/
https://www.ncbi.nlm.nih.gov/pubmed/36525461
http://dx.doi.org/10.1371/journal.pone.0277232
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