Conference Agenda

Missing values in multivariate dependent data are common in many applied settings and pose challenges for standard imputation methods, particularly when complex dependence structures are present. We introduce NPCoImp, a nonparametric copula-based approach for imputing multivariate missing data. The method relies on the empirical beta copula to estimate conditional distribution functions of missing variables given the observed ones, allowing the imputation process to account for the radial symmetry or asymmetry of the joint dependence structure. NPCoImp is highly flexible and can accommodate arbitrary missingness patterns in multivariate settings. We assess its performance through an extensive Monte Carlo simulation study, comparing it with classical imputation methods, the CoImp algorithm, and the machine-learning-based missForest approach. The results show that NPCoImp performs particularly well in preserving dependence structures across different sample sizes, missingness levels, and dependence strengths. The practical relevance of the method is illustrated through applications to real data from the agricultural sector.