Optimal strategy for improved estimation of population mean of sensitive variable using non-sensitive auxiliary variable
Abstract To improve the transformed ratio type estimators, this study uses new population parameters that are derived from extra information using a randomized response technique (RRT). Additionally, we suggest a modified family of powerful estimators for estimating the population mean of the sensit...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-04-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-024-01045-x |
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| author | Abdullah A. Zaagan Dinesh K. Sharma Ali M. Mahnashi Mutum Zico Meetei Subhash Kumar Yadav Aakriti Sharma Pranav Sharma |
| author_facet | Abdullah A. Zaagan Dinesh K. Sharma Ali M. Mahnashi Mutum Zico Meetei Subhash Kumar Yadav Aakriti Sharma Pranav Sharma |
| author_sort | Abdullah A. Zaagan |
| collection | DOAJ |
| description | Abstract To improve the transformed ratio type estimators, this study uses new population parameters that are derived from extra information using a randomized response technique (RRT). Additionally, we suggest a modified family of powerful estimators for estimating the population mean of the sensitive variable in the presence of auxiliary data that are not sensitive. The bias and mean squared error (MSE), which are the primary statistical characteristics of the proposed estimator, have been determined up to the first order of approximation. We conduct theoretical comparisons among the contending estimators. Theoretical claims are supported by empirical evidence obtained from actual datasets. The suggested and competing estimators are further compared by analyzing their performances on a simulated data set. For a wide range of sensitive research applications, it is advisable to choose an estimator that possesses desirable sample properties and a minimized mean squared error (MSE). |
| format | Article |
| id | doaj-art-45491e0430b945ca99d85bbe49c4aff3 |
| institution | OA Journals |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-45491e0430b945ca99d85bbe49c4aff32025-08-20T02:11:11ZengSpringerOpenJournal of Big Data2196-11152025-04-0112111910.1186/s40537-024-01045-xOptimal strategy for improved estimation of population mean of sensitive variable using non-sensitive auxiliary variableAbdullah A. Zaagan0Dinesh K. Sharma1Ali M. Mahnashi2Mutum Zico Meetei3Subhash Kumar Yadav4Aakriti Sharma5Pranav Sharma6Department of Mathematics, College of Science, Jazan UniversityDepartment of Business, Management and Accounting, University of Maryland Eastern ShoreDepartment of Mathematics, College of Science, Jazan UniversityDepartment of Mathematics, College of Science, Jazan UniversityDepartment of Statistics, Babasaheb Bhimrao Ambedkar UniversityDepartment of Statistics, Babasaheb Bhimrao Ambedkar UniversityDepartment of Statistics, Babasaheb Bhimrao Ambedkar UniversityAbstract To improve the transformed ratio type estimators, this study uses new population parameters that are derived from extra information using a randomized response technique (RRT). Additionally, we suggest a modified family of powerful estimators for estimating the population mean of the sensitive variable in the presence of auxiliary data that are not sensitive. The bias and mean squared error (MSE), which are the primary statistical characteristics of the proposed estimator, have been determined up to the first order of approximation. We conduct theoretical comparisons among the contending estimators. Theoretical claims are supported by empirical evidence obtained from actual datasets. The suggested and competing estimators are further compared by analyzing their performances on a simulated data set. For a wide range of sensitive research applications, it is advisable to choose an estimator that possesses desirable sample properties and a minimized mean squared error (MSE).https://doi.org/10.1186/s40537-024-01045-xSensitive variableScrambled responseRandomized response techniqueBiasMSERatio estimator |
| spellingShingle | Abdullah A. Zaagan Dinesh K. Sharma Ali M. Mahnashi Mutum Zico Meetei Subhash Kumar Yadav Aakriti Sharma Pranav Sharma Optimal strategy for improved estimation of population mean of sensitive variable using non-sensitive auxiliary variable Journal of Big Data Sensitive variable Scrambled response Randomized response technique Bias MSE Ratio estimator |
| title | Optimal strategy for improved estimation of population mean of sensitive variable using non-sensitive auxiliary variable |
| title_full | Optimal strategy for improved estimation of population mean of sensitive variable using non-sensitive auxiliary variable |
| title_fullStr | Optimal strategy for improved estimation of population mean of sensitive variable using non-sensitive auxiliary variable |
| title_full_unstemmed | Optimal strategy for improved estimation of population mean of sensitive variable using non-sensitive auxiliary variable |
| title_short | Optimal strategy for improved estimation of population mean of sensitive variable using non-sensitive auxiliary variable |
| title_sort | optimal strategy for improved estimation of population mean of sensitive variable using non sensitive auxiliary variable |
| topic | Sensitive variable Scrambled response Randomized response technique Bias MSE Ratio estimator |
| url | https://doi.org/10.1186/s40537-024-01045-x |
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