Optimal neutrosophic framework for population mean  estimation under simple random sampling

Anoop Kumar; Priya; Vrijesh Tripathi

Authors

Anoop Kumar Department of Statistics, Central University of Haryana, Mahendergarh, Haryana, India, 123031.
Priya Department of Statistics, Central University of Haryana, Mahendergarh, Haryana, India, 123031
Vrijesh Tripathi Depatment of Mathematics and Statistics, The University of The West Indies St. Augustine, Trinidad and Tobago.

Keywords:

Mean square error; Neutrosophic framework; Bivariate auxiliary information; E ciency.

Abstract

When conducting survey sampling, precise population mean estimation is essential, particularly
when additional indeterminate data is available. The intrinsic ambiguity and uncertainty in the study and aux
iliary variables are handled by using neutrosophic logic, which consists of truth, indeterminacy, and falsehood.
In this paper, we extend the classical estimation techniques by incorporating bivariate auxiliary information
within the neutrosophic framework, o ering an optimal neutrosophic framework for population mean estimation
under simple random sampling (SRS). Through simulation experiments and real-life datasets, the e ectiveness
of the proposed optimal neutrosophic framework is evaluated and compared with the adapted neutrosophic
frameworks. The outcomes demonstrate that the suggested optimal neutrosophic framework demonstrates re
duced mean square error (MSE) and enhanced e ciency in comparison to the adapted neutrosophic frameworks.

DOI: 10.5281/zenodo.15514518