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- MakaleYalnız künye2026Scopus SJRQ1A novel neutrosophic estimation method for handling uncertainty in population mean estimation
Yadav, Akhilesh; Yadav, Dharmendra Kumar; Yadav, Subhash Kumar; Zaman, Tolga
Estimating the population mean using auxiliary information has been extensively explored within the classical framework. While point estimators offer simplicity, they provide only a single value without reflecting the uncertainty or variability inherent in real-world data. This shortcoming is particularly critical in high-precision applications and decision-making contexts. Moreover, classical estimators are often vulnerable to the influence of outliers, which can distort results and introduce significant bias. To address these limitations, this study introduces a novel estimator grounded in neutrosophic theory, which is specifically designed to handle imprecise, vague, and incomplete information-conditions frequently encountered in practical sampling scenarios. The proposed neutrosophic estimator incorporates auxiliary information expressed in neutrosophic terms, offering a more flexible and resilient estimation strategy. We derive expressions for the bias and mean square error of the proposed estimator under a first-order approximation. A comprehensive theoretical analysis, supported by simulation studies, confirms that the neutrosophic estimator consistently outperforms classical estimators in terms of accuracy and robustness, especially in uncertain environments. These findings underscore the potential of neutrosophic approaches as powerful alternatives to conventional estimation techniques in modern statistical inference.
AtıfScopus2OpenAlex3İncele - MakaleYalnız künye2026Scopus SJRQ1Quantile-based neutrosophic regression estimators under stratified random sampling
Zaman, Tolga; Sozen, Caglar
The neutrosophic framework provides a convenient way to represent uncertainty and indeterminacy through interval-valued quantities. This paper proposes quantile-based neutrosophic combined and separate regression estimators for the finite population mean under stratified random sampling. Building on the combined and separate quantile regression estimators of Koç and Koç (Axioms 12:713, 2023), we incorporate neutrosophic (interval) versions of stratum-specific quantile regression components and derive approximate bias and mean squared error (MSE) expressions using first-order linearization. We provide efficiency conditions under which the proposed neutrosophic estimators improve upon their classical counterparts and we evaluate performance via (i) a simulation study comparing classical and neutrosophic estimators, and (ii) a real-data illustration based on monthly high–low index data (2010–2024) for the S&P 500, Nikkei 225, and DAX, where MSE bounds are reported for the proposed neutrosophic estimators. Across the considered experiments, the neutrosophic separate estimator at the 0.50 quantile attains the smallest MSE among the reported competitors, suggesting that a median-oriented robust component combined with stratification can be effective when observations are imprecise. Limitations and directions for further extensions are discussed.
AtıfScopus1İncele - MakaleAçık Erişim2025Scopus SJRQ2Neutrosophic exponential estimators for estimating the population mean using auxiliary attributes: An application to temperature data
Zaman, Tolga; Sozen, Caglar
Classical statistical methods cannot directly incorporate measurement uncertainties and indeterministic elements into the model, leading to estimation errors and biases, especially in environmental datasets. Neutrosophic statistics, on the other hand, offers a broader approach that does not only rely on exact values, but also includes ambiguous, incomplete and conflicting information. In this study, we propose a new neutrosophic ratio-type estimator for estimating the population mean using monthly temperature data for Turkey for the period 2010-2022. This study proposes neutrosophic multivariate exponential estimators considering two auxiliary attributes. The mean square error equations of all proposed neutrosophic exponential estimators are obtained theoretically. Some conditions of neutrosophic multivariate exponential estimators are considered and the properties of these estimators are studied. The effectiveness of the proposed neutrosophic exponential estimators is analyzed using neutrosophic temperature data. In addition to real data analysis, extensive simulation studies are conducted under various scenarios to evaluate the performance of the estimators. The results demonstrate that the proposed estimators perform effectively under uncertainty and can be successfully applied in forecasting environmental variables.
AtıfScopus2WoS1OpenAlex1İncele