Enhanced Neutrosophic Estimation Procedures for Population Mean under Simple Random Sampling: Insights from Stock Market Analysis

Accepted January 2026

Abstract

Neutrosophic estimators provide a strong foundation for mean estimation in sample surveys where data may be ambiguous, inconsistent, or incomplete. These estimators improve the dependability of the estimates by taking into account the indeterminate character of data, which lowers the possibility of risk of biased results. This article develops enhanced neutrosophic estimation procedure for accurately measuring the population mean employing simple random sampling (SRS). The proposed neutrosophic estimator exhibits superior performance in relation to the contemporary neutrosophic estimators. A broad spectrum simulation study validates the theoretical benefits of the newly developed estimators, highlighting their accuracy and robustness. Further, we execute
the enhanced estimation procedure to stock market data, showing its practical applicability and elevated accuracy in real-life financial analysis. The findings underscore the potential of the proposed neutrosophic estimators over the neutrosophic mean-per-unit estimator, neutrosophic ratio estimator, neutrosophic modified ratio estimators, neutrosophic regression estimator, neutrosophic
logarithmic estimator, neutrosophic exponential estimator, Tahir et al. (2021) estimator, Yadav and Smarandache (2023) estimator, and Yadav and Prasad (2024) estimator in terms of lesser mean square error and higher percent relative efficiency.