A Novel Family of Hybrid Neutrosophic  Estimators for Population Mean Estimation

Anisha Taneja; Sachin Malik

Authors

Anisha Taneja Department of Mathematics SRM University Delhi-NCR, Sonepat, Haryana, India
Sachin Malik Department of Mathematics SRM University Delhi-NCR, Sonepat, Haryana, India

Keywords:

Neutrosophic statistics, Hybrid estimators, Population mean, Ratio-product estimation, Exponential estimation, Classical statis tics comparison

Abstract

This paper introduces a novel family of hybrid neutrosophic esti
mators for estimating the population mean when dealing with inde
terminate data. Building upon neutrosophic statistics, we propose
two innovative estimators that synergistically combine ratio-product
and exponential components to enhance estimation accuracy. The
proposed estimators integrate the strengths of existing neutrosophic
ratio, product, and exponential estimators while incorporating op
timization parameters. We derive the theoretical properties of the
proposed estimators, including bias and mean squared error (MSE),
and obtain optimal expressions for the parameters. Through an ex
tensive empirical study using real neutrosophic data from medical
sales and networking, we demonstrate the superior performance of
our proposed estimators compared to existing alternatives. The re
sults show significant improvements in relative efficiency compared
to the conventional mean estimator, particularly when dealing with
highly correlated neutrosophic variables. Additionally, we conduct
a comprehensive comparison with classical statistical methods, re
vealing that our neutrosophic approach provides more accurate and
robust estimates than classical methods. This research contributes
to the advancement of neutrosophic statistics by providing more reliable tools for population mean estimation in uncertain environ
ments