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<p>This study introduces a new neutrosophic-type estimator for estimating the population mean in stratified sampling, motivated by the growing need for reliable statistical tools that can handle uncertainty, imprecision, and incomplete information. By combining auxiliary variables with neutrosophic theory, the proposed estimator is able to capture indeterminacy more effectively and produce meaningful interval-based estimates. We derive its bias and mean squared error (MSE) and compare its performance with several existing estimators. The results show that the new estimator consistently achieves lower MSE and much higher percentage relative efficiency (PRE), demonstrating clear improvements in accuracy. Numerical examples further confirm its advantages. The method is especially useful for data that naturally involve uncertainty or interval measurements, such as environmental indicators, financial data, sensor readings, and medical diagnostics. Overall, the study provides a more robust and efficient estimation approach for modern datasets where uncertainty cannot be ignored.</p>

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Neutrosophic approaches to population mean estimation in stratified sampling: application to the indian temperature records

  • 期刊论文

  • 发表日期：

    2026年2月21日

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  • Mukesh Kumar Verma,
  • Subhash Kumar Yadav,
  • Rohini Yadav,
  • Sandeep Kumar Yadav

摘要

This study introduces a new neutrosophic-type estimator for estimating the population mean in stratified sampling, motivated by the growing need for reliable statistical tools that can handle uncertainty, imprecision, and incomplete information. By combining auxiliary variables with neutrosophic theory, the proposed estimator is able to capture indeterminacy more effectively and produce meaningful interval-based estimates. We derive its bias and mean squared error (MSE) and compare its performance with several existing estimators. The results show that the new estimator consistently achieves lower MSE and much higher percentage relative efficiency (PRE), demonstrating clear improvements in accuracy. Numerical examples further confirm its advantages. The method is especially useful for data that naturally involve uncertainty or interval measurements, such as environmental indicators, financial data, sensor readings, and medical diagnostics. Overall, the study provides a more robust and efficient estimation approach for modern datasets where uncertainty cannot be ignored.