Neutrosophic Quantile Regression for Robust Median Estimation: Application to Stock Price Fluctuations

In this study, we introduced a novel generalized class of ratio-type estimators for median estimation by employing quantile regression within the framework of neutrosophic statistics is designed to enhance the accuracy and reliability of estimates in the presence of data uncertainty, providing a robust alternative to traditional point estimators. The proposed methodology yields interval-based estimates for the population median, capturing a range of possible values rather than a point estimate, which allows modeling uncertainty and partial truth inherent in complex or imprecise data. This is achieved within the neutrosophic statistical framework, a generalization of classical statistics that explicitly accounts for indeterminacy and inconsistent information, making it well-suited for handling ambiguous data in interdisciplinary applications. We validate the performance of our estimators using real-life stock price data from Samsung Electronics Co. Ltd. (SMSN.IL), sourced from Yahoo Finance (2022), and further substantiate the results through a comprehensive simulation study. Comparisons between traditional and proposed estimators, based on mean squared error, percentage decrease in mean squared error, and a simulation study, demonstrate the superior precision and reliability of our approach.