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Imputation for Skewed Data: Multivariate Lomax Case

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  • Zhixin Lun

    (Oakland University
    University of California)

  • Ravindra Khattree

    (Oakland University)

Abstract

Most multiple imputation methods for multivariate missing data have been developed for normally distributed data. However, methods may not be suitable for nonnegative and/or highly skewed data. We propose an approach by using Expectation-Maximization (EM) method based on the assumption of multivariate Lomax distribution on non-negative skewed data. Extensive simulations show that this proposed method outperforms the regular normality-based EM and k-nearest-neighbor (k NN) imputation methods under the missing completely at random (MCAR) mechanism. An application on a real-world biomedical data is then provided.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:sankhb:v:83:y:2021:i:1:d:10.1007_s13571-021-00251-4
    DOI: 10.1007/s13571-021-00251-4
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    References listed on IDEAS

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    3. 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.
    4. 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.
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