تقرير
Vine copula mixture models and clustering for non-Gaussian data
العنوان: | Vine copula mixture models and clustering for non-Gaussian data |
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المؤلفون: | Sahin, Özge, Czado, Claudia |
سنة النشر: | 2021 |
المجموعة: | Statistics |
مصطلحات موضوعية: | Statistics - Methodology, Statistics - Machine Learning |
الوصف: | 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. |
نوع الوثيقة: | Working Paper |
DOI: | 10.1016/j.ecosta.2021.08.011 |
URL الوصول: | http://arxiv.org/abs/2102.03257 |
رقم الأكسشن: | edsarx.2102.03257 |
قاعدة البيانات: | arXiv |
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