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<p>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&amp;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.</p>

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Quantile-based neutrosophic regression estimators under stratified random sampling

  • 期刊论文

  • 发表日期：

    2026年3月7日

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  • Tolga Zaman,
  • Caglar Sozen

摘要

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.