A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling
In this article, we proposed an improved finite population variance estimator based on simple random sampling using dual auxiliary information. Mathematical expressions of the proposed and existing estimators are obtained up to the first order of approximation. Two real data sets are used to examine...
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| Veröffentlicht in: | PloS one 2022-11, Vol.17 (11), p.e0276540 |
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| creator | Ahmad, Sohaib Hussain, Sardar Ullah, Kalim Zahid, Erum Aamir, Muhammad Shabbir, Javid Ahmad, Zubair Alshanbari, Huda M Alajlan, Wejdan |
| description | In this article, we proposed an improved finite population variance estimator based on simple random sampling using dual auxiliary information. Mathematical expressions of the proposed and existing estimators are obtained up to the first order of approximation. Two real data sets are used to examine the performances of a new improved proposed estimator. A simulation study is also recognized to assess the robustness and generalizability of the proposed estimator. From the result of real data sets and simulation study, it is examining that the proposed estimator give minimum mean square error and percentage relative efficiency are higher than all existing counterparts, which shown the importance of new improved estimator. The theoretical and numerical result illustrated that the proposed variance estimator based on simple random sampling using dual auxiliary information has the best among all existing estimators. |
| doi_str_mv | 10.1371/journal.pone.0276540 |
| format | Article |
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Mathematical expressions of the proposed and existing estimators are obtained up to the first order of approximation. Two real data sets are used to examine the performances of a new improved proposed estimator. A simulation study is also recognized to assess the robustness and generalizability of the proposed estimator. From the result of real data sets and simulation study, it is examining that the proposed estimator give minimum mean square error and percentage relative efficiency are higher than all existing counterparts, which shown the importance of new improved estimator. The theoretical and numerical result illustrated that the proposed variance estimator based on simple random sampling using dual auxiliary information has the best among all existing estimators.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0276540</identifier><identifier>PMID: 36399453</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Biology and Life Sciences ; Computer Simulation ; Data Collection ; Datasets ; Efficiency ; Estimators ; Mathematical analysis ; Mathematical research ; Mean square errors ; Medicine and Health Sciences ; Methods ; Models, Statistical ; People and Places ; Physical Sciences ; Random sampling ; Research and Analysis Methods ; Research Design ; Robustness (mathematics) ; Sampling ; Simulation ; Social surveys ; Statistical sampling ; Variables ; Variance</subject><ispartof>PloS one, 2022-11, Vol.17 (11), p.e0276540</ispartof><rights>Copyright: © 2022 Ahmad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Ahmad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Mathematical expressions of the proposed and existing estimators are obtained up to the first order of approximation. Two real data sets are used to examine the performances of a new improved proposed estimator. A simulation study is also recognized to assess the robustness and generalizability of the proposed estimator. From the result of real data sets and simulation study, it is examining that the proposed estimator give minimum mean square error and percentage relative efficiency are higher than all existing counterparts, which shown the importance of new improved estimator. The theoretical and numerical result illustrated that the proposed variance estimator based on simple random sampling using dual auxiliary information has the best among all existing estimators.</description><subject>Biology and Life Sciences</subject><subject>Computer Simulation</subject><subject>Data Collection</subject><subject>Datasets</subject><subject>Efficiency</subject><subject>Estimators</subject><subject>Mathematical analysis</subject><subject>Mathematical research</subject><subject>Mean square errors</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Models, Statistical</subject><subject>People and Places</subject><subject>Physical Sciences</subject><subject>Random sampling</subject><subject>Research and Analysis Methods</subject><subject>Research Design</subject><subject>Robustness (mathematics)</subject><subject>Sampling</subject><subject>Simulation</subject><subject>Social surveys</subject><subject>Statistical 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Wejdan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-11-18</date><risdate>2022</risdate><volume>17</volume><issue>11</issue><spage>e0276540</spage><pages>e0276540-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>In this article, we proposed an improved finite population variance estimator based on simple random sampling using dual auxiliary information. Mathematical expressions of the proposed and existing estimators are obtained up to the first order of approximation. Two real data sets are used to examine the performances of a new improved proposed estimator. A simulation study is also recognized to assess the robustness and generalizability of the proposed estimator. From the result of real data sets and simulation study, it is examining that the proposed estimator give minimum mean square error and percentage relative efficiency are higher than all existing counterparts, which shown the importance of new improved estimator. The theoretical and numerical result illustrated that the proposed variance estimator based on simple random sampling using dual auxiliary information has the best among all existing estimators.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36399453</pmid><doi>10.1371/journal.pone.0276540</doi><tpages>e0276540</tpages><orcidid>https://orcid.org/0000-0003-3782-4081</orcidid><orcidid>https://orcid.org/0000-0001-9274-0396</orcidid><orcidid>https://orcid.org/0000-0001-5154-7477</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Biology and Life Sciences Computer Simulation Data Collection Datasets Efficiency Estimators Mathematical analysis Mathematical research Mean square errors Medicine and Health Sciences Methods Models, Statistical People and Places Physical Sciences Random sampling Research and Analysis Methods Research Design Robustness (mathematics) Sampling Simulation Social surveys Statistical sampling Variables Variance |
| title | A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling |
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