A conditional copula-based imputation technique
Di Lascio FML
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We present a copula-based imputation method for multivariate missing data with generic patterns and complex dependence structure. The method is based on the conditional density functions of the missing variables given the observed ones. These functions can be derived analytically once parametric models for the margins and the copula are specified. When analytical derivations are not feasible, the margins are estimated non-parametrically through local likelihood methods. We describe both the analytic and the semiparametric version of the the copula-based imputa-tion method and investigate their performance in terms of preservation of both the dependence structure and the microdata through Monte Carlo studies. Moreover,we present a comparison with classical imputation methods. We have implemented and made available the method through the R packageCoImp. We provide an illustration of how to handle the imputation through the R package, i.e. a description of its main functions, their output and usage on real data sets.