Imputing continuous data under some non-Gaussian distributions

There has been a growing interest regarding generalized classes ofdistributions in statistical theory and practice because of their flexibil-ityinmodelformation.Multipleimputationundersuchdistributionsthatspan a broader area in the symmetry–kurtosis plane appears to havethepotentialofbettercapturingrealincompletedatatrends.Inthisarti-cle, we impute continuous univariate data that exhibit varying charac-teristicsundertwowell-knowndistributions,assesstheextenttowhichthisprocedureworksproperly,makecomparisonswithnormalimputa-tion models in terms of commonly accepted bias and precision mea-sures, and discuss possible generalizations to the multivariate caseand to larger families of distributions.