Affiliations: [a] Department of Statistics, TU Dortmund University, Dortmund, Germany | [b] Research Center Trustworthy Data Science and Security, University Alliance Ruhr, Dortmund, Germany | [c] Federal Statistical Office of Germany (Destatis), Wiesbaden, Germany
Correspondence:
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Corresponding author: Maria Thurow, Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany. E-mail: maria.thurow@tu-dortmund.de.
Abstract: Imputation methods are popular tools that allow for a wide range of subsequent analyses on complete data sets. However, in order for these analyses to be trustworthy, it is important that the imputation procedure reflects the true distribution of the unobserved data sufficiently well. This raises the question how well different imputation methods can reproduce multivariate correlations, associations or even the entire multivariate distribution. The paper gives first answers to this question by means of an extensive comparative simulation study. In particular, we evaluate the multivariate distributional accuracy for six state-of-the art imputation algorithms with respect to different measures and give practical recommendations.
