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Novel imputation methods under stratified simple random sampling
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DOI：10.1016/j.aej.2024.03.088.png)
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摘要
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This paper addresses some classes of combined and separate imputation methods (CSIMs) of the population mean under stratified simple random sampling (SSRS) along with their characteristics. To the best of our knowledge, these imputation methods (IMs) have yet not been studied by any author under SSRS, hence these IMs are called 'novel'. In addition, the existing CSIMs are distinguished as the members of the suggested CSIMs, respectively. The theoretical conditions under which the proposed IMs perform better are obtained by comparing the proposed IMs with the existing IMs. To validate the theoretical findings, the numerical and simulation studies are conducted on real and artificial populations, respectively.
Keyword:
Missing values
Imputation
Stratified simple random sampling
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