Neutrosophic imputation: a novel approach to population mean estimation under indeterminacy
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Date
2026
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Abstract
Accurate population mean estimation is crucial in survey sampling, particularly when dealing with missing or partial data.
The intrinsic uncertainty and indeterminacy found in these datasets frequently pose challenges for computational impu
tation approaches. In this article, by including neutrosophic logic into the imputation process, some novel neutrosophic
imputation approaches and their corresponding resultant neutrosophic estimators for population mean estimation are pre
sented along with their properties under simple random sampling (SRS). Neutrosophic logic offers a strong foundation for
overcoming the drawbacks of conventional approaches because of its capacity to handle indeterminate and uncertain data.
We show that the proposed novel neutrosophic imputation performs better than the conventional neutrosophic imputation
approaches through comprehensive simulations and practical experiments. The simulation and practical results demon
strate how the proposed approaches may be used to increase the precision of statistical studies, particularly in domains
where ambiguity and partial data are common.