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- MakaleYalnız künye2026A 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.
- MakaleYalnız künye2026Quantile-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.
- MakaleYalnız künye2026Minimum covariance determinant-based estimators for estimating population mean using two auxiliary variables
Zaman, Tolga; Islamoglu, Ebrucan; Bulut, Hasan
This study addresses the challenge of outliers in sample surveys, a common issue that can distort results and lead to inaccurate conclusions. To tackle this problem, researchers have developed various robust regression techniques including least trimmed squares, least median of squares, least absolute deviations, Huber’s, Hample’s and Tukey’s maximum likelihood estimators, and Huber’s maximum likelihood estimator for location and scale estimators. We propose a new approach: minimum covariance determinant-based ratio estimators for estimating the population mean in sample surveys. To assess the performance of the proposed minimum covariance determinant-based estimators, we derive their mean square error expressions and determine the conditions under which they surpass existing estimators in efficiency. To validate our theoretical findings, we conduct a numerical illustration using real-world data and perform simulations with R-Programme software. The results demonstrate that our method effectively handles outliers and improves the overall accuracy of survey-based estimations, making it a valuable tool for researchers working with sample survey data.