English

Matrix and graph representations of vine copula structures

Machine Learning 2023-03-14 v2 Machine Learning

Abstract

Vine copulas can efficiently model multivariate probability distributions. This paper focuses on a more thorough understanding of their structures, since in the literature, vine copula representations are often ambiguous. The graph representations include the original, cherry and chordal graph sequence structures, which we show equivalence between. Importantly we also show a new result, namely that when a perfect elimination ordering of a vine structure is given, then it can always be uniquely represented with a matrix. O. M. N\'apoles has shown a way to represent vines in a matrix, and we algorithmify this previous approach, while also showing a new method for constructing such a matrix, through cherry tree sequences. We also calculate the runtime of these algorithms. Lastly, we prove that these two matrix-building algorithms are equivalent if the same perfect elimination ordering is being used.

Cite

@article{arxiv.2205.04783,
  title  = {Matrix and graph representations of vine copula structures},
  author = {Dániel Pfeifer and Edith Alice Kovács},
  journal= {arXiv preprint arXiv:2205.04783},
  year   = {2023}
}

Comments

23 pages, 27 figures

R2 v1 2026-06-24T11:12:53.068Z