A family of estimators for variance estimation
The potential of this study for estimating the finite population variance of the study variable of a class of estimators by utilizing an auxiliary variable in simple random sampling is enormous. The asymptotic properties of the proposed class of estimation procedure have been examined. The best fixe...
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| Format: | Article |
| Language: | English |
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Taylor & Francis
2024-12-01
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| Series: | Research in Statistics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/27684520.2024.2350750 |
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| author | Housila P. Singh Sanam Preet Kour Sunil Kumar Rakesh Chib |
| author_facet | Housila P. Singh Sanam Preet Kour Sunil Kumar Rakesh Chib |
| author_sort | Housila P. Singh |
| collection | DOAJ |
| description | The potential of this study for estimating the finite population variance of the study variable of a class of estimators by utilizing an auxiliary variable in simple random sampling is enormous. The asymptotic properties of the proposed class of estimation procedure have been examined. The best fixed values are determined for which the mean squared error of the freshly suggested estimator is lowest. Several well-known existing estimators and class of suggested estimators are identified. To back up the theoretical results, a real population and an extensive simulation study using R software are performed, which demonstrate the dominance of the suggested estimator against all competitors. Appropriate suggestions have been made to the survey statisticians for their real-life implementation. |
| format | Article |
| id | doaj-art-87cc47bc4c2746009d1d20ef97fe01c6 |
| institution | Kabale University |
| issn | 2768-4520 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis |
| record_format | Article |
| series | Research in Statistics |
| spelling | doaj-art-87cc47bc4c2746009d1d20ef97fe01c62024-12-02T19:00:47ZengTaylor & FrancisResearch in Statistics2768-45202024-12-012110.1080/27684520.2024.2350750A family of estimators for variance estimationHousila P. Singh0Sanam Preet Kour1Sunil Kumar2Rakesh Chib3School of Studies in Statistics, Vikram University, Ujjain, MP, IndiaDepartment of Statistics, University of Jammu, Jammu, JK, IndiaDepartment of Statistics, University of Jammu, Jammu, JK, IndiaDepartment of Statistics, University of Jammu, Jammu, JK, IndiaThe potential of this study for estimating the finite population variance of the study variable of a class of estimators by utilizing an auxiliary variable in simple random sampling is enormous. The asymptotic properties of the proposed class of estimation procedure have been examined. The best fixed values are determined for which the mean squared error of the freshly suggested estimator is lowest. Several well-known existing estimators and class of suggested estimators are identified. To back up the theoretical results, a real population and an extensive simulation study using R software are performed, which demonstrate the dominance of the suggested estimator against all competitors. Appropriate suggestions have been made to the survey statisticians for their real-life implementation.https://www.tandfonline.com/doi/10.1080/27684520.2024.2350750Auxiliary variablestudy variablesimple random samplingbiasmean squared errorclass of estimators |
| spellingShingle | Housila P. Singh Sanam Preet Kour Sunil Kumar Rakesh Chib A family of estimators for variance estimation Research in Statistics Auxiliary variable study variable simple random sampling bias mean squared error class of estimators |
| title | A family of estimators for variance estimation |
| title_full | A family of estimators for variance estimation |
| title_fullStr | A family of estimators for variance estimation |
| title_full_unstemmed | A family of estimators for variance estimation |
| title_short | A family of estimators for variance estimation |
| title_sort | family of estimators for variance estimation |
| topic | Auxiliary variable study variable simple random sampling bias mean squared error class of estimators |
| url | https://www.tandfonline.com/doi/10.1080/27684520.2024.2350750 |
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