The New Sub-regression Type Estimator in Ranked Set Sampling

In this study, a new sub-regression type estimator for ranked set sampling (RSS) is proposed based on the idea of a sub-ratio estimator given in Koçyiğit and Kadılar (Commun Stat Theory Methods 1–23, 2022). The proposed unbiased estimator's mean square error is obtained and compared theoretical...

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Detalles Bibliográficos
Autores principales: Koçyiğit, Eda Gizem, Rather, Khalid Ul Islam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9974047/
https://www.ncbi.nlm.nih.gov/pubmed/36875336
http://dx.doi.org/10.1007/s42519-023-00324-9
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author Koçyiğit, Eda Gizem
Rather, Khalid Ul Islam
author_facet Koçyiğit, Eda Gizem
Rather, Khalid Ul Islam
author_sort Koçyiğit, Eda Gizem
collection PubMed
description In this study, a new sub-regression type estimator for ranked set sampling (RSS) is proposed based on the idea of a sub-ratio estimator given in Koçyiğit and Kadılar (Commun Stat Theory Methods 1–23, 2022). The proposed unbiased estimator's mean square error is obtained and compared theoretically with other estimators. The theoretical results have been supported by the different simulations and real-life data sets studies and have shown that the proposed estimator is more effective than the estimators in the literature. It is also seen that the number of repetitions in the RSS affected the effectiveness of the sub-estimators.
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spelling pubmed-99740472023-03-01 The New Sub-regression Type Estimator in Ranked Set Sampling Koçyiğit, Eda Gizem Rather, Khalid Ul Islam J Stat Theory Pract Original Article In this study, a new sub-regression type estimator for ranked set sampling (RSS) is proposed based on the idea of a sub-ratio estimator given in Koçyiğit and Kadılar (Commun Stat Theory Methods 1–23, 2022). The proposed unbiased estimator's mean square error is obtained and compared theoretically with other estimators. The theoretical results have been supported by the different simulations and real-life data sets studies and have shown that the proposed estimator is more effective than the estimators in the literature. It is also seen that the number of repetitions in the RSS affected the effectiveness of the sub-estimators. Springer International Publishing 2023-02-28 2023 /pmc/articles/PMC9974047/ /pubmed/36875336 http://dx.doi.org/10.1007/s42519-023-00324-9 Text en © Grace Scientific Publishing 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
Koçyiğit, Eda Gizem
Rather, Khalid Ul Islam
The New Sub-regression Type Estimator in Ranked Set Sampling
title The New Sub-regression Type Estimator in Ranked Set Sampling
title_full The New Sub-regression Type Estimator in Ranked Set Sampling
title_fullStr The New Sub-regression Type Estimator in Ranked Set Sampling
title_full_unstemmed The New Sub-regression Type Estimator in Ranked Set Sampling
title_short The New Sub-regression Type Estimator in Ranked Set Sampling
title_sort new sub-regression type estimator in ranked set sampling
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9974047/
https://www.ncbi.nlm.nih.gov/pubmed/36875336
http://dx.doi.org/10.1007/s42519-023-00324-9
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