Imputing continuous data under some non-Gaussian distributions

There has been a growing interest regarding generalized classes of distributions in statistical theory and practice because of their flexibility in model formation. Multiple imputation under such distributions that span a broader area in the symmetry-kurtosis plane appears to have the potential of better capturing real incomplete data trends. In this article, we impute continuous univariate data that exhibit varying characteristics under two well-known distributions, assess the extent to which this procedure works properly, make comparisons with normal imputation models in terms of commonly accepted bias and precision measures, and discuss possible generalizations to the multivariate case and to larger families of distributions.