Estimation of population mean using generalized neutrosophic exponential estimators

Abstract

In classical statistics, the data we examine is precise and determinate, resulting in a specific, single value. However, when the data are unclear, ambiguous or in the form of an interval, such as observing daily stock prices or daily temperatures in a city, we cannot rely on classical statistics. In such situations, neutrosophic statistics are far more dependable. In this article, we propose three generalized neutrosophic exponential estimators for the population mean using neutrosophic subsidiary information. The expression for the bias and mean square error of the suggested estimators is computed using a first-order approximation. Then to demonstrate the properties of the suggested neutrosophic estimators, real life neutrosophic data sets based on product sales and marketing in the field of medical are utilized. Additionally, we conducted a simulation study, demonstrating that our proposed estimators outperform the estimators currently described in this article. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.