Some Optimal Classes of Estimators Based on Multi-Auxiliary Information
Ranked set sampling (RSS) has been proven an efficient alternative to simple random sampling (SRS). The use of auxiliary information also helps to improve the efficiency of the estimation procedures. Therefore, to accomplish higher efficiency and discuss the optimality issues, we proffer some optima...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/12/6/515 |
| _version_ | 1827738613093236736 |
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| author | Shashi Bhushan Anoop Kumar Najwan Alsadat Manahil SidAhmed Mustafa Meshayil M. Alsolmi |
| author_facet | Shashi Bhushan Anoop Kumar Najwan Alsadat Manahil SidAhmed Mustafa Meshayil M. Alsolmi |
| author_sort | Shashi Bhushan |
| collection | DOAJ |
| description | Ranked set sampling (RSS) has been proven an efficient alternative to simple random sampling (SRS). The use of auxiliary information also helps to improve the efficiency of the estimation procedures. Therefore, to accomplish higher efficiency and discuss the optimality issues, we proffer some optimal classes of estimators under RSS by employing multi-auxiliary information. It is seen that the ordinary mean estimator, traditional regression, and ratio estimators are the subsets of the proffered estimators. The expressions of the bias and mean square error are reported. An analytical comparison under some optimality conditions points out the ascendancy of the proffered classes of estimators over all reviewed works. The theoretical results have been furnished with computational study by employing some artificial and natural populations. The computational results show that the proffered estimators outperform the conventional estimators reviewed in this study. Furthermore, apposite advices are suggested to the survey persons. |
| first_indexed | 2024-03-11T02:46:49Z |
| format | Article |
| id | doaj.art-7579f1766f7f44d0931ccde74e10e7aa |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-11T02:46:49Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-7579f1766f7f44d0931ccde74e10e7aa2023-11-18T09:16:08ZengMDPI AGAxioms2075-16802023-05-0112651510.3390/axioms12060515Some Optimal Classes of Estimators Based on Multi-Auxiliary InformationShashi Bhushan0Anoop Kumar1Najwan Alsadat2Manahil SidAhmed Mustafa3Meshayil M. Alsolmi4Department of Statistics, University of Lucknow, Lucknow 226007, IndiaDepartment of Statistics, Amity University, Lucknow 226028, IndiaDepartment of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh 11587, Saudi ArabiaDepartment of Statistics, Faculty of Science, University of Tabuk, Tabuk 47713, Saudi ArabiaDepartment of Mathematics, College of Science and Arts at Khulis, University of Jeddah, Jeddah 22233, Saudi ArabiaRanked set sampling (RSS) has been proven an efficient alternative to simple random sampling (SRS). The use of auxiliary information also helps to improve the efficiency of the estimation procedures. Therefore, to accomplish higher efficiency and discuss the optimality issues, we proffer some optimal classes of estimators under RSS by employing multi-auxiliary information. It is seen that the ordinary mean estimator, traditional regression, and ratio estimators are the subsets of the proffered estimators. The expressions of the bias and mean square error are reported. An analytical comparison under some optimality conditions points out the ascendancy of the proffered classes of estimators over all reviewed works. The theoretical results have been furnished with computational study by employing some artificial and natural populations. The computational results show that the proffered estimators outperform the conventional estimators reviewed in this study. Furthermore, apposite advices are suggested to the survey persons.https://www.mdpi.com/2075-1680/12/6/515multi-auxiliary informationefficiencymean square errorranked set sampling |
| spellingShingle | Shashi Bhushan Anoop Kumar Najwan Alsadat Manahil SidAhmed Mustafa Meshayil M. Alsolmi Some Optimal Classes of Estimators Based on Multi-Auxiliary Information Axioms multi-auxiliary information efficiency mean square error ranked set sampling |
| title | Some Optimal Classes of Estimators Based on Multi-Auxiliary Information |
| title_full | Some Optimal Classes of Estimators Based on Multi-Auxiliary Information |
| title_fullStr | Some Optimal Classes of Estimators Based on Multi-Auxiliary Information |
| title_full_unstemmed | Some Optimal Classes of Estimators Based on Multi-Auxiliary Information |
| title_short | Some Optimal Classes of Estimators Based on Multi-Auxiliary Information |
| title_sort | some optimal classes of estimators based on multi auxiliary information |
| topic | multi-auxiliary information efficiency mean square error ranked set sampling |
| url | https://www.mdpi.com/2075-1680/12/6/515 |
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