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Missing data is a common problem in clinical data collection, which causesdifficulty in the statistical analysis of such data. To overcome problemscaused by incomplete data, we propose a new imputation method called projectiveresampling imputation mean estimation (PRIME), which can also address ``thecurse of dimensionality" problem in imputation with less information loss. Weuse various sample sizes, missing-data rates, covariate correlations, and noiselevels in simulation studies, and all results show that PRIME outperformesother methods such as iterative least-squares estimation (ILSE), maximumlikelihood (ML), and complete-case analysis (CC). Moreover, we conduct a studyof influential factors in cardiac surgery-associated acute kidney injury(CSA-AKI), which show that our method performs better than the other models.Finally, we prove that PRIME has a consistent property under some regularconditions.

Projective Resampling Imputation Mean Estimation Method for Missing Covariates Problem