Vine copula mixture models and clustering for non-Gaussian data

Özge Sahin and Claudia Czado

Econometrics and Statistics, 2022, vol. 22, issue C, 136-158

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 dependencies, a novel vine copula mixture model for continuous data is proposed. The model selection and parameter estimation problems are discussed, and further, a new model-based clustering algorithm is formulated. The use of vine copulas in clustering allows for a range of shapes and dependency structures for the clusters. The 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. The model-based clustering algorithm with vine copula mixture models outperforms others, especially for the non-Gaussian multivariate data.

Keywords: Dependence; ECM algorithm; model-based clustering; multivariate finite mixtures; pair-copula; statistical learning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:22:y:2022:i:c:p:136-158

DOI: 10.1016/j.ecosta.2021.08.011

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