ORIGINAL RESEARCH article
Front. Appl. Math. Stat.
Sec. Statistics and Probability
Volume 11 - 2025 | doi: 10.3389/fams.2025.1658157
Neutrosophic Regression Type Estimator for the Finite Population Mean and its Applications in Real Data Scenario
Provisionally accepted
- 1Ph.D. Scholar, The Graduate School, ICAR-Indian Agricultural Research Institute, New Delhi, New Delhi, India
- 2Division of Sample Surveys, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, Senior Scientist, New delhi, India
- 3Scientist, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, New Delhi, India
- 4Head, Division of Sample Surveys, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, New Delhi, India
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Research under classical statistics often relies on precise, determinate data to estimate population parameters. However, in certain situations, data may be indeterminate or imprecise. Neutrosophic statistics, a generalization of classical statistics, has been introduced to address these challenges by handling vague, indeterminate, and uncertain information effectively. Several estimators, including ratio estimators, have been proposed in neutrosophic statistics. These ratio estimators perform well when the correlation between the auxiliary and study variables is strong. However, in this study, regression-type estimators were developed, demonstrating superior performance in cases where the correlation between the study and auxiliary variables is high, weak, or moderate. The performance of the proposed estimator was evaluated using simulated data as well as four real-world datasets with indeterminate data, including blood pressure, temperature, natural growth rate, and solar energy data. The proposed neutrosophic regression estimator consistently performed better than existing neutrosophic ratio estimators, modified neutrosophic ratio estimators, and neutrosophic exponential ratio estimators, as indicated by performance measures mean square error (MSE) and percent relative efficiency (PRE). This paper highlights the advantages of the neutrosophic regression estimator in improving estimation accuracy when dealing with uncertain and ambiguous data with any range of correlation between the study and the auxiliary variables considered under the study.
Keywords: Classical statistics, Neutrosophic statistics, Neutrosophic estimator, Bias, Percent Relative Efficiency (PRE)
Received: 02 Jul 2025; Accepted: 18 Aug 2025.
Copyright: © 2025 Purwar, Aditya, ., Ahmad and Das. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Kaustav Aditya, Division of Sample Surveys, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, Senior Scientist, New delhi, India
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