# Vine copula mixture models and clustering for non-Gaussian data

arXiv: https://arxiv.org/abs/2102.03257 · HTML: https://mlsanity.com/paper/2102.03257/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 65 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [A Mixture of Generalized Hyperbolic Distributions](https://mlsanity.com/paper/1305.1036/) (2013)
- [Parsimonious mixtures of multivariate contaminated normal distributions](https://mlsanity.com/paper/1305.4669/) (2013)
- [Mixtures of Multivariate Power Exponential Distributions](https://mlsanity.com/paper/1506.04137/) (2015)
- [Model selection in sparse high-dimensional vine copula models with application to portfolio risk](https://mlsanity.com/paper/1801.09739/) (2018)
- [Model-based clustering using copulas with applications](https://mlsanity.com/paper/1404.4077/) (2014)
- [Improved initialisation of model-based clustering using Gaussian hierarchical partitions](https://mlsanity.com/paper/1507.07114/) (2015)
- [Variable selection for clustering with Gaussian mixture models: state of the art](https://mlsanity.com/paper/1701.08946/) (2017)
- [A Mixture of SDB Skew-t Factor Analyzers](https://mlsanity.com/paper/1310.6224/) (2013)

## Built on by (most cited first, 2 of 2)

- [Vine Copulas as Differentiable Computational Graphs](https://mlsanity.com/paper/2506.13318/) (2025)
- [Empirical Density Estimation based on Spline Quasi-Interpolation with applications to Copulas clustering modeling](https://mlsanity.com/paper/2402.11552/) (2024)

## Abstract (arXiv)

The majority of finite mixture models suffer from not allowing asymmetric tail dependencies within components and not capturing non-elliptical clusters in clustering applications. Since vine copulas are very flexible in capturing these types of dependencies, we propose a novel vine copula mixture model for continuous data. We discuss the model selection and parameter estimation problems and further formulate a new model-based clustering algorithm. The use of vine copulas in clustering allows for a range of shapes and dependency structures for the clusters. Our simulation experiments illustrate a significant gain in clustering accuracy when notably asymmetric tail dependencies or/and non-Gaussian margins within the components exist. The analysis of real data sets accompanies the proposed method. We show that the model-based clustering algorithm with vine copula mixture models outperforms the other model-based clustering techniques, especially for the non-Gaussian multivariate data.