Developing a Generalized Class of Estimators for Estimation of Population Mean using Neutrosophic Approach

Abstract

Preceding studies have only been conducted with clear, determinate data, as the classical statistics approach is unable to accommodate ambiguity and uncertainty. As a generalization and alternative to classical statistics for such indeterminate or uncertain data, neutrosophic statistics is concerned with dealing such ambiguity in data. Making use of neutrosophic data, we propose a generalized class of estimators for the population mean under simple random sampling. Based on the numerical outcome it is presented that the suggested estimators achieved well in terms of minimum MSE. In order to more accurately represent the range of values throughout which our population parameter occurs, the results of these estimators are provided as intervals rather than a single value. We use simulation and interval data from the Islamabad Stock Exchange, focused on the UBL, to further investigate the efficiency of the suggested neutrosophic estimator. The numerical results confirm the suggested generalized neutrosophic estimators are superior to the existing methods. The mean square error (MSE) and percentage relative efficiency (PRE) performance measures demonstrate that the developed neutrosophic regression type estimator is always better than the conventional neutrosophic ratio estimator, neutrosophic product estimators, and the neutrosophic exponential ratio estimator. This paper addresses concerning how the neutrosophic regression estimator can make estimates more accurate when working with data that is unclear or uncertain and has a wide range of correlation between the study and the auxiliary variables that are being examined.