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Article Type

Research Article

Abstract

In classical statistics, population mean estimation generally assumes precise and determinate data along with known auxiliary information. However, in real-world situations where observations are imprecise or expressed in interval form, such as temperature variations or financial market data, classical approaches become less effective. To address this limitation, neutrosophic statistics provide a more flexible framework for handling uncertainty and indeterminacy. This study proposes a neutrosophic logarithmic ratio-product type estimator for estimating the finite population mean using auxiliary information. The bias and mean squared error (MSE) of the proposed estimator are derived using a first-order approximation. Furthermore, performance evaluation is carried out using MSE and percentage relative efficiency (PRE), showing the superiority of the proposed estimator over existing methods. The study also presents graphical illustrations through bar charts depicting the lower and upper bounds of MSE values for different estimators across datasets. Empirical analysis using real-life neutrosophic data, including COVID-19 observations, demonstrates the practical effectiveness and robustness of the proposed estimator.

Keywords

Classical statistics, Neutrosophic statistics, Logarithmic-ratio-product-type estimator, Auxiliary information, Mean squared error (MSE), Percentage relative efficiency (PRE)

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