Vine copula mixture models and clustering for non-Gaussian data

• URL: http://arxiv.org/abs/2102.03257v1
• Date: Fri, 5 Feb 2021 16:04:26 GMT
• Title: Vine copula mixture models and clustering for non-Gaussian data
• Authors: \"Ozge Sahin, Claudia Czado
• Abstract summary: We propose a novel vine copula mixture model for continuous data. We show that the model-based clustering algorithm with vine copula mixture models outperforms the other model-based clustering techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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