Design based synthetic imputation methods for domain mean
Abstract In real life, situations may arise when the available data are insufficient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-53909-0 |
| _version_ | 1797274915750019072 |
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| author | Shashi Bhushan Anoop Kumar Rohini Pokhrel M. E. Bakr Getachew Tekle Mekiso |
| author_facet | Shashi Bhushan Anoop Kumar Rohini Pokhrel M. E. Bakr Getachew Tekle Mekiso |
| author_sort | Shashi Bhushan |
| collection | DOAJ |
| description | Abstract In real life, situations may arise when the available data are insufficient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has an impact on sample surveys, but small area estimates are especially prone to it. This paper is a basic effort that suggests design based synthetic imputation methods for the domain mean estimation using simple random sampling in order to address the issue of missing data under SAE. The expression of the mean square error for the proposed imputation methods are obtained up to first order approximation. The efficiency conditions are determined and a thorough simulation study is carried out using artificially generated data sets. An application is included with real data that further supports this study. |
| first_indexed | 2024-03-07T15:05:01Z |
| format | Article |
| id | doaj.art-1f6e2671766741de9f6018912aa24230 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:05:01Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-1f6e2671766741de9f6018912aa242302024-03-05T18:54:53ZengNature PortfolioScientific Reports2045-23222024-02-0114111110.1038/s41598-024-53909-0Design based synthetic imputation methods for domain meanShashi Bhushan0Anoop Kumar1Rohini Pokhrel2M. E. Bakr3Getachew Tekle Mekiso4Department of Statistics, University of LucknowDepartment of Statistics, Faculty of Basic Science, Central University of HaryanaDepartment of Mathematics and Statistics, Dr. Shakuntala Misra National Rehabilitation UniversityDepartment of Statistics and Operations Research, College of Science, King Saud UniversityDepartment of Statistics, Wachemo UniversityAbstract In real life, situations may arise when the available data are insufficient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has an impact on sample surveys, but small area estimates are especially prone to it. This paper is a basic effort that suggests design based synthetic imputation methods for the domain mean estimation using simple random sampling in order to address the issue of missing data under SAE. The expression of the mean square error for the proposed imputation methods are obtained up to first order approximation. The efficiency conditions are determined and a thorough simulation study is carried out using artificially generated data sets. An application is included with real data that further supports this study.https://doi.org/10.1038/s41598-024-53909-0Small area estimationMissing valueImputationEfficiency |
| spellingShingle | Shashi Bhushan Anoop Kumar Rohini Pokhrel M. E. Bakr Getachew Tekle Mekiso Design based synthetic imputation methods for domain mean Scientific Reports Small area estimation Missing value Imputation Efficiency |
| title | Design based synthetic imputation methods for domain mean |
| title_full | Design based synthetic imputation methods for domain mean |
| title_fullStr | Design based synthetic imputation methods for domain mean |
| title_full_unstemmed | Design based synthetic imputation methods for domain mean |
| title_short | Design based synthetic imputation methods for domain mean |
| title_sort | design based synthetic imputation methods for domain mean |
| topic | Small area estimation Missing value Imputation Efficiency |
| url | https://doi.org/10.1038/s41598-024-53909-0 |
| work_keys_str_mv | AT shashibhushan designbasedsyntheticimputationmethodsfordomainmean AT anoopkumar designbasedsyntheticimputationmethodsfordomainmean AT rohinipokhrel designbasedsyntheticimputationmethodsfordomainmean AT mebakr designbasedsyntheticimputationmethodsfordomainmean AT getachewteklemekiso designbasedsyntheticimputationmethodsfordomainmean |