Neutrosophic ratio-type exponential estimators for estimation of population mean

The current work is one step in filling a large void in the research left by the advent of neutrosophic Statistics (NS), a philosophized variant of classical statistics (CS). The philosophy of NS deals with techniques for investigating data that is ambiguous, hazy, or uncertain. The traditional techniques of estimation utilizing auxiliary information work under specific determinate data, which in the case of neutrosophic data may lead to mistakes (over/ under-estimation). This study presents a generalized neutrosophic ratio-type exponential estimator (NRTEE) for estimating location parameters and achieving the lowest mean square error (MSE) possible for interval neutrosophic data (IND). The offered NRTEE helps to deal with the uncertainty and ambiguity of data. Unlike typical estimators, its findings are not single-valued but rather in interval form, which reduces the possibility of over-or under-estimation caused by single crisp outcomes and also increases the likelihood of the parameter dwelling in the interval. It improves the efficiency of the estimator since we have an estimated interval that contains the unknown value of the population mean with a minimal MSE. The suggested NRTEE's efficiency is further addressed by utilizing real-life IND of temperature and simulations. A comparison is also performed to establish the superiority of the proposed estimator over the traditional estimators. The limits are calculated and discussed in cases when our suggested estimator is always efficient. The suggested estimator is the most efficient of all estimators and outperformed all others on both IND and classical data.

Automated summary

This study presents a generalized neutrosophic ratio-type exponential estimator (NRTEE) for estimating location parameters and achieving the lowest mean square error (MSE) possible for interval neutrosophic data (IND). Unlike typical estimators, its findings are not single-valued but rather in interval form, which reduces the possibility of over-or under-estimation caused by single crisp outcomes and also increases the likelihood of the parameter dwelling in the interval. The suggested NRTEE's efficiency is further addressed by utilizing real-life IND of temperature and simulations.