Computation of separate ratio and regression estimator under Neutrosophic stratified sampling: an application to climate data
In this article, we introduce a novel approach by presenting separate ratio and regression estimators in the context of neutrosophic stratified sampling for the very first time, incorporating auxiliary variables. We have conducted a thorough analysis to estimate these newly proposed estimators'...
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| Format: | Article |
| Language: | English |
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Ayandegan Institute of Higher Education,
2024-12-01
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| Series: | Journal of Fuzzy Extension and Applications |
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| Online Access: | https://www.journal-fea.com/article_193258_412d4a77ab016a006c679638a2ceacfb.pdf |
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| author | Abhishek Singh Hemant Kulkarni Florentin Smarandache Gajendra Vishwakarma |
| author_facet | Abhishek Singh Hemant Kulkarni Florentin Smarandache Gajendra Vishwakarma |
| author_sort | Abhishek Singh |
| collection | DOAJ |
| description | In this article, we introduce a novel approach by presenting separate ratio and regression estimators in the context of neutrosophic stratified sampling for the very first time, incorporating auxiliary variables. We have conducted a thorough analysis to estimate these newly proposed estimators' bias and Mean Square Error (MSE) up to the first-order approximation. Theoretically using efficiency comparison criteria, our findings demonstrate the superior performance of these estimators compared to traditional unbiased estimators. Also, numerically based on real-life and artificial data, we have shown the supremacy of the neutrosophic stratified sampling over neutrosophic simple random sampling along with the supremacy of our proposed neutrosophic separate stratified estimators over neutrosophic stratified unbiased estimator. Moreover, our research highlights the enhanced reliability of neutrosophic stratified estimators when contrasted with classical stratified estimators. |
| format | Article |
| id | doaj-art-74b7ae95674846fb97c840c85f615719 |
| institution | Kabale University |
| issn | 2783-1442 2717-3453 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ayandegan Institute of Higher Education, |
| record_format | Article |
| series | Journal of Fuzzy Extension and Applications |
| spelling | doaj-art-74b7ae95674846fb97c840c85f6157192025-01-30T15:07:17ZengAyandegan Institute of Higher Education,Journal of Fuzzy Extension and Applications2783-14422717-34532024-12-015460562110.22105/jfea.2024.422211.1313193258Computation of separate ratio and regression estimator under Neutrosophic stratified sampling: an application to climate dataAbhishek Singh0Hemant Kulkarni1Florentin Smarandache2Gajendra Vishwakarma3Department of Mathematics and Statistics, Dr.Vishwanath Karad MIT World Peace University, Kothrud, Pune, Maharashtra, India.Department of Mathematics and Statistics, Dr.Vishwanath Karad MIT World Peace University, Kothrud, Pune, Maharashtra, India.Mathematics, Physical and Natural Science Division, University of New Mexico, Gallup, 705 Gurley Ave., Gallup, NM 87301, USA.Department of Mathematics and Computing, Indian Institute of Technology (ISM) Dhanbad, Dhanbad-826004, Jharkhand, India.In this article, we introduce a novel approach by presenting separate ratio and regression estimators in the context of neutrosophic stratified sampling for the very first time, incorporating auxiliary variables. We have conducted a thorough analysis to estimate these newly proposed estimators' bias and Mean Square Error (MSE) up to the first-order approximation. Theoretically using efficiency comparison criteria, our findings demonstrate the superior performance of these estimators compared to traditional unbiased estimators. Also, numerically based on real-life and artificial data, we have shown the supremacy of the neutrosophic stratified sampling over neutrosophic simple random sampling along with the supremacy of our proposed neutrosophic separate stratified estimators over neutrosophic stratified unbiased estimator. Moreover, our research highlights the enhanced reliability of neutrosophic stratified estimators when contrasted with classical stratified estimators.https://www.journal-fea.com/article_193258_412d4a77ab016a006c679638a2ceacfb.pdfneutrosophic variablesneutrosophic stratified samplingregression and ratio estimatormonte-carlo simulationmean square error |
| spellingShingle | Abhishek Singh Hemant Kulkarni Florentin Smarandache Gajendra Vishwakarma Computation of separate ratio and regression estimator under Neutrosophic stratified sampling: an application to climate data Journal of Fuzzy Extension and Applications neutrosophic variables neutrosophic stratified sampling regression and ratio estimator monte-carlo simulation mean square error |
| title | Computation of separate ratio and regression estimator under Neutrosophic stratified sampling: an application to climate data |
| title_full | Computation of separate ratio and regression estimator under Neutrosophic stratified sampling: an application to climate data |
| title_fullStr | Computation of separate ratio and regression estimator under Neutrosophic stratified sampling: an application to climate data |
| title_full_unstemmed | Computation of separate ratio and regression estimator under Neutrosophic stratified sampling: an application to climate data |
| title_short | Computation of separate ratio and regression estimator under Neutrosophic stratified sampling: an application to climate data |
| title_sort | computation of separate ratio and regression estimator under neutrosophic stratified sampling an application to climate data |
| topic | neutrosophic variables neutrosophic stratified sampling regression and ratio estimator monte-carlo simulation mean square error |
| url | https://www.journal-fea.com/article_193258_412d4a77ab016a006c679638a2ceacfb.pdf |
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