Branch Mathematics and Statistics Faculty and Staff Publications
Document Type
Article
Publication Date
2024
Abstract
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.
Publication Title
Journal of Fuzzy Extension and Applications
Language (ISO)
English
Keywords
Neutrosophic variables, Neutrosophic stratified sampling, Regression and ratio estimator, Monte-Carlo simulation, Mean square error
Recommended Citation
Singh, Abhishek; Hemant Kulkarni; Florentin Smarandache; and Gajendra K. Vishwakarma. "Computation of Separate Ratio and Regression Estimator Under Neutrosophic Stratified Sampling: An Application to Climate Data." Journal of Fuzzy Extension and Applications (2024). https://digitalrepository.unm.edu/math_fsp/696
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
