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Article Type

Research Article

Abstract

In classical statistical theory, estimation of population parameters is generally carried out under the assumption that all observed data are precise, complete, and free from ambiguity. However, in many practical and real-world situations, data often deviate from these ideal conditions and instead appear in vague, uncertain, or interval-valued forms. Such imperfections reduce the effectiveness of traditional estimation techniques and motivate the development of more flexible and robust methodologies. To address these challenges, several improved estimators, particularly neutrosophic ratio-type estimators and their advanced extensions, have been proposed in recent literature. In this study, a new estimator known as the two auxiliary variable exponential neutrosophic estimator is introduced for estimating the population mean under uncertain and indeterminate environments. The proposed estimator simultaneously incorporates information from two auxiliary variables, which significantly enhances its efficiency and reliability compared to existing approaches. The statistical properties of the estimator are evaluated using standard performance measures such as Mean Square Error (MSE) and Percentage Relative Efficiency (PRE). Additionally, a comprehensive simulation study is conducted to examine the robustness of the estimator under varying conditions and sample structures. The simulation results are supported with graphical representations of MSE values, which clearly illustrate the performance advantage of the proposed estimator. Furthermore, the practical applicability of the estimator is validated using real agricultural data. The empirical results, along with simulation findings, consistently demonstrate that the proposed estimator outperforms existing neutrosophic estimators in terms of efficiency and stability. These findings confirm its suitability for handling uncertain and imprecise data situations. Overall, the study highlights that the proposed methodology provides a more reliable tool for population mean estimation in indeterminate environments.

Keywords

Classical statistics, Neutrosophic statistics, Exponential type estimator, Auxiliary information, Bias, Mean squared error (MSE), Percentage relative efficiency (PRE)

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