Vine Copula Model: Application to Chemical Elements in Water Samples


  • Salsabila Zahra Aminullah Universitas Indonesia
  • Mila Novita Universitas Indonesia
  • Ida Fithriani Universitas Indonesia



Akaike information criterion, Archimedean copula, dependence structure, elliptical copula, multivariate model, pseudo-maximum likelihood, sequential estimation


Copula can link the bivariate distribution function with marginal distribution functions without requiring specific information about the interdependence among random variables. There are several types of copulas, such as elliptical copulas, Archimedean copulas, and extreme value copulas. However, in multivariate modeling, each type of copula has limitations in modeling complex dependence structures in terms of symmetry and tail dependence properties. The class of vine copulas overcomes these limitations by constructing multivariate models using bivariate copulas in a tree-like structure. The bivariate copulas used in this study include the Clayton, Gumbel, Frank, Gaussian, and Student's t copula families. This study discusses the construction of vine copula models, parameter estimation, and their applications. The construction of vine copulas is done through the decomposition of conditional probability density functions and substituting bivariate copula density functions into the decomposition results. The data used in the study is the logarithm of the concentration of chemical elements in water samples in Colorado. The parameter estimation method used is pseudo-maximum likelihood with sequential estimation. Model selection is then performed using the Akaike information criterion (AIC) to determine the most suitable model. The results indicate that Caesium and Titanium have a dependency relationship with Scandium. Moreover, Scandium and Titanium exhibit the strongest dependence compared to other variable pairs.




How to Cite

Aminullah, S. Z., Novita, M., & Fithriani, I. (2023). Vine Copula Model: Application to Chemical Elements in Water Samples. Proceedings of The International Conference on Data Science and Official Statistics, 2023(1), 572–585.