A Nonparametric Copula-Based Imputation Method

Missing values in multivariate dependent variables may occur during data collection, requiring imputation methods capable of handling complex inter-variable relationships. We propose a nonparametric copula-based method for imputing dependent multivariate missing data, called NPCoImp. By leveraging the empirical beta copula to calculate the conditional cumulative probability function of the missing variables given the observed ones, NPCoImp imputes data while respecting the underlying distributional shape - particularly radial symmetry - and accordingly adjusts the multivariate values used for imputation. NPCoImp is highly flexible and can handle multivariate missing data with any type of missingness pattern. The performance of NPCoImp has been evaluated through an extensive Monte Carlo study and compared with classical imputation methods, as well as with its direct competitor, the CoImp algorithm, and a machine learning-based technique, the missForest. Our findings indicate that NPCoImp is particularly effective in preserving complex dependence structure irrespective of the sample size, the proportion of missing data and the strength of the dependence. The strong performance of the proposed method is further supported by empirical case studies in the agricultural sector. Finally, the NPCoImp algorithm has been implemented in the R package CoImp, which is available on CRAN.