New comprehensive mean estimation using regression-cum-exponential type estimator: Application with neutrosophic data
When the point estimator is used to estimate population parameters, it provides a single value. In such a scenario, the neutrosophic method is beneficial for estimating the parameters of interest in sampling theory as it yields interval estimates where the parameter value mainly originates. Neutroso...
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Elsevier
2025-01-01
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| Series: | Kuwait Journal of Science |
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| Online Access: | https://www.sciencedirect.com/science/article/pii/S2307410824001718 |
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| description | When the point estimator is used to estimate population parameters, it provides a single value. In such a scenario, the neutrosophic method is beneficial for estimating the parameters of interest in sampling theory as it yields interval estimates where the parameter value mainly originates. Neutrosophic statistics focuses on uncertain or imprecise data. In this article, we suggest a new enhanced neutrosophic class of estimators to estimate the population mean. The properties (bias and mean squared error) are derived from the first-degree approximation. The suggested estimators are useful when working with uncertain, unclear, neutrosophic-type data. The best possible values of the defining scalars characterizing constants and the minimum neutrosophic mean squared error (MSE) for the suggested estimators are determined for these ideal values. Neutrosophic estimators outperform their classical counterparts because the existing estimated interval includes the minimum MSE when estimating the population mean. We use a simulation study and a real dataset from the Islamabad Stock Exchange. Variations in parameter and estimator combinations are reflected in the MSE values. From the numerical results, the estimators Y‾ˆPN, Y‾ˆSKN, and Y‾ˆAN have substantially higher MSE values, suggesting more significant estimation error. The estimators Y═ˆGPi (i = 1, 2, 3, 4, and 5) show better accuracy performance with relatively minimum MSE values. The numerical outcome shows that the suggested classes of estimators perform well as compared to the existing estimators. © 2024 The Authors |
| format | Article |
| id | doaj-art-52ab67f749d343b58df4e2e886c1c08e |
| institution | OA Journals |
| issn | 2307-4108 2307-4116 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Kuwait Journal of Science |
| spelling | doaj-art-52ab67f749d343b58df4e2e886c1c08e2025-08-20T02:10:28ZengElsevierKuwait Journal of Science2307-41082307-41162025-01-0152110034610.1016/j.kjs.2024.100346New comprehensive mean estimation using regression-cum-exponential type estimator: Application with neutrosophic dataWhen the point estimator is used to estimate population parameters, it provides a single value. In such a scenario, the neutrosophic method is beneficial for estimating the parameters of interest in sampling theory as it yields interval estimates where the parameter value mainly originates. Neutrosophic statistics focuses on uncertain or imprecise data. In this article, we suggest a new enhanced neutrosophic class of estimators to estimate the population mean. The properties (bias and mean squared error) are derived from the first-degree approximation. The suggested estimators are useful when working with uncertain, unclear, neutrosophic-type data. The best possible values of the defining scalars characterizing constants and the minimum neutrosophic mean squared error (MSE) for the suggested estimators are determined for these ideal values. Neutrosophic estimators outperform their classical counterparts because the existing estimated interval includes the minimum MSE when estimating the population mean. We use a simulation study and a real dataset from the Islamabad Stock Exchange. Variations in parameter and estimator combinations are reflected in the MSE values. From the numerical results, the estimators Y‾ˆPN, Y‾ˆSKN, and Y‾ˆAN have substantially higher MSE values, suggesting more significant estimation error. The estimators Y═ˆGPi (i = 1, 2, 3, 4, and 5) show better accuracy performance with relatively minimum MSE values. The numerical outcome shows that the suggested classes of estimators perform well as compared to the existing estimators. © 2024 The Authorshttps://www.sciencedirect.com/science/article/pii/S2307410824001718biasmean estimationmean square errorneutrosophic statisticspercentage relative efficiencysimulation |
| spellingShingle | New comprehensive mean estimation using regression-cum-exponential type estimator: Application with neutrosophic data Kuwait Journal of Science bias mean estimation mean square error neutrosophic statistics percentage relative efficiency simulation |
| title | New comprehensive mean estimation using regression-cum-exponential type estimator: Application with neutrosophic data |
| title_full | New comprehensive mean estimation using regression-cum-exponential type estimator: Application with neutrosophic data |
| title_fullStr | New comprehensive mean estimation using regression-cum-exponential type estimator: Application with neutrosophic data |
| title_full_unstemmed | New comprehensive mean estimation using regression-cum-exponential type estimator: Application with neutrosophic data |
| title_short | New comprehensive mean estimation using regression-cum-exponential type estimator: Application with neutrosophic data |
| title_sort | new comprehensive mean estimation using regression cum exponential type estimator application with neutrosophic data |
| topic | bias mean estimation mean square error neutrosophic statistics percentage relative efficiency simulation |
| url | https://www.sciencedirect.com/science/article/pii/S2307410824001718 |