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Imputing continuous data under some non‐Gaussian distributions

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  • Hakan Demirtas
  • Donald Hedeker

Abstract

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.

Suggested Citation

  • Hakan Demirtas & Donald Hedeker, 2008. "Imputing continuous data under some non‐Gaussian distributions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(2), pages 193-205, May.
    • Handle: RePEc:bla:stanee:v:62:y:2008:i:2:p:193-205
    DOI: 10.1111/j.1467-9574.2007.00377.x
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    Cited by:

    1. Paul T. von Hippel, 2013. "Should a Normal Imputation Model be Modified to Impute Skewed Variables?," Sociological Methods & Research, , vol. 42(1), pages 105-138, February.
    2. Yulei He & Trivellore E. Raghunathan, 2012. "Multiple imputation using multivariate gh transformations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(10), pages 2177-2198, June.
    3. Zhixin Lun & Ravindra Khattree, 2021. "Imputation for Skewed Data: Multivariate Lomax Case," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 86-113, May.
    4. Rüdiger Mutz & Lutz Bornmann & Hans-Dieter Daniel, 2015. "Testing for the fairness and predictive validity of research funding decisions: A multilevel multiple imputation for missing data approach using ex-ante and ex-post peer evaluation data from the Austr," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2321-2339, November.
    5. Marco Geraci & Alexander McLain, 2018. "Multiple Imputation for Bounded Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 919-940, December.

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