Real-World Implementation of a Neutrosophic Logarithmic Estimator for Population Mean Estimation under Stratified Random Sampling

Deep Shikha; Shakti Prasad; Deepak Majhi; Housila P. Singh

Authors

Deep Shikha Department of Mathematics, National Institute of Technology Jamshedpur, Jamshedpur-831014, Jharkhand, India.
Shakti Prasad Department of Mathematics, National Institute of Technology Jamshedpur, Jamshedpur-831014, Jharkhand, India.
Deepak Majhi PG Department of Mathematics, Veer Kunwar Singh University, Ara- 802301, Bihar, India.
Housila P. Singh Retired, School of Studies in Statistics, Vikram University, Ujjain-456010, M.P., India.

Keywords:

Bias, Neutrosophic Estimator, Mean Squared Error, Percent Relative Efficiency

Abstract

This paper addresses the problem of utilizing auxiliary information to estimate the population
mean under neutrosophic stratified random sampling. Within the framework of neutrosophic statistics, we
propose a neutrosophic combined logarithmic type estimator that effectively accounts for the uncertainty and
indeterminacy inherent in survey sampling. The suggested estimator’s bias and mean squared error (MSE) are
expressed up to the first order of approximation, and ideal circumstances for reducing the MSE are determined.
Neutroposophic combined mean, neutrosophic combined ratio, and neutrosophic combined difference estimators
are theoretically compared with the suggested estimator.The analytical findings show that in some real-world
situations, the suggested estimator performs better than its conventional competitors. Additionally, line and
radar chart representations and empirical data analysis are used to assess the performance of the suggested
estimator. The results provide useful information for practitioners and survey statisticians who use neutrosophic
stratified random sampling when uncertainty is present.

DOI 10.5281/zenodo.20609978