A nonparametric conditional copula-based imputation method

Missing values in multivariate dependent variables may occur during data collection, requiring imputation methods capable of handling complex intervariable relationships. We propose a nonparametric copula-based method for imputing dependent multivariate missing data, called NPCoImp. By leveraging the conditional empirical beta copula of the missing variables given the observed ones, NPCoImp imputes data while accounting for its distributional shape, particularly radial symmetry, and adjusting the multivariate values used for imputation accordingly. NPCoImp is highly exible and can handle multivariate missing data with any type of missingness pattern. The performance of the 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. Our findings indicate that NPCoImp is particularly effective in preserving microdata and dependence structure. 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.