Neutrosophic Exponential Ratio-Type Estimator for Finite Population Mean

This paper proposes a neutrosophic exponential-type estimator for finite population mean estimation using auxiliary variables. Traditional statistical estimators often fall short when handling vague or uncertain data. Neutrosophic statistics provide a robust alternative, as they are specifically designed to address and incorporate indeterminacy. The mean square error (MSE) expressions are derived and the proposed estimator is compared with existing estimators through a numerical example using stock price data and a simulation study. Unlike traditional techniques, which provide point estimates, this method yields interval-based results and achieves a lower mean squared error (MSE), thereby enhancing the accuracy and dependability of the population mean estimation.