Neutrosophic ranked set sampling imputation methods for estimating imprecise population mean under missing data
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Abstract
In survey sampling, estimating the population mean properly in the case of missing data is a difficult issue, while it becomes much more typical if the data are imprecise or uncertain. The conventional imputation methods frequently fail to handle the underlying uncertainty and imprecision in real-life datasets. To solve this issue, this paper adapts some fundamental neutrosophic imputations and proposes some efficient neutrosophic imputations under neutrosophic ranked set sampling (NeRSS) that successfully handles imprecise data while using neutrosophic logic principles. Through theoretical analysis and simulation study, we show that the proposed imputation methods yield more robust and trustworthy population mean estimates than the adapted ones, particularly in datasets with high missingness and imprecision. A real data insight is also presented in support of the theoretical and simulation findings.
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