Uncertainty Based Efficient Neutrosophic Imputation Methods for Population Mean

Abstract

In survey sampling, imputation methods are essential for handling missing data which can have a large impact on statistical analysis and inference. Neutrosophic logic is an extension of the classical logic which provides a robust framework for tackling indeterminate, inconsistent, and incomplete information. In this article, some uncertainty based efficient neutrosophic imputation methods (ENIMs) and the resultant efficient neutrosophic estimators are developed to estimate the population mean under simple random sampling (SRS). The bias and mean square error (MSE) of the resultant neutrosophic estimators are obtained to the first order approximation. The proposed ENIMs are evaluated through extensive simulations and real data applications, demonstrating superior performance in terms of reduced MSE and increased percent relative efficiency (PRE). The findings of this study suggest that neutrosophic imputation methods (NIMs) offer a significant advancement in survey sampling methodologies, enhancing the accuracy of statistical inferences in the presence of incomplete data.