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Vine copula statistical disclosure control for mixed-type data

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  • Chu, Amanda M.Y.
  • Ip, Chun Yin
  • Lam, Benson S.Y.
  • So, Mike K.P.

Abstract

In this paper, we develop a new statistical disclosure control (SDC) method for mixed-type data based on vine copulas. The use of Gaussian and skew-t copulas has been demonstrated to be capable of incorporating information from the marginal distributions of mixed-type variables, whether they are discrete or continuous. In particular, our proposed SDC method using vine copulas generalizes a data perturbation method using an extended skew-t copula. Our vine-SDC method improves the SDC method using the extended skew-t copula by allowing the bivariate copulas in the vine decomposition to take various forms, thus offering a better fit for the joint distribution of the data and more flexibility in data perturbation. An additional advantage of our vine-SDC method is the significant improvement in computational efficiency compared with that using the extended skew-t copula. We discuss some statistical properties of vine copulas and the methodology of vine-SDC. A simulation and a study of real healthcare survey data are provided to explore the performance and strength of vine-SDC and compare it with a common copula-based SDC method.

Suggested Citation

  • Chu, Amanda M.Y. & Ip, Chun Yin & Lam, Benson S.Y. & So, Mike K.P., 2022. "Vine copula statistical disclosure control for mixed-type data," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    • Handle: RePEc:eee:csdana:v:176:y:2022:i:c:s0167947322001414
    DOI: 10.1016/j.csda.2022.107561
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    References listed on IDEAS

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