Vine copula statistical disclosure control for mixed-type data
From MaRDI portal
Publication:2674512
DOI10.1016/j.csda.2022.107561OpenAlexW4283800313MaRDI QIDQ2674512
Mike K. P. So, Benson S. Y. Lam, Chun Yin Ip, Amanda M. Y. Chu
Publication date: 14 September 2022
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2022.107561
Cites Work
- Unnamed Item
- Releasing Multiply Imputed, Synthetic Public use Microdata: An Illustration and Empirical Study
- Pair-copula constructions of multiple dependence
- Selecting and estimating regular vine copulae and application to financial returns
- Maintaining tail dependence in data shuffling using \(t\) copula
- Optimal noise addition for preserving confidentiality in multivariate data
- Data-swapping: A technique for disclosure control
- Vine-copula GARCH model with dynamic conditional dependence
- Comorbidity of chronic diseases in the elderly: patterns identified by a copula design for mixed responses
- Vines -- a new graphical model for dependent random variables.
- Probability density decomposition for conditionally dependent random variables modeled by vines
- Truncation of vine copulas using fit indices
- Perturbation of Numerical Confidential Data via Skew-t Distributions
- A D‐vine copula‐based model for repeated measurements extending linear mixed models with homogeneous correlation structure
- Distributions Generated by Perturbation of Symmetry with Emphasis on a Multivariate Skewt-Distribution
- Accessibility, Security, and Accuracy in Statistical Databases: The Case for the Multiplicative Fixed Data Perturbation Approach
- k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
- Measures of multivariate skewness and kurtosis with applications
This page was built for publication: Vine copula statistical disclosure control for mixed-type data
