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Title:

Vine copula mixture models and clustering for non-Gaussian data

Document type:
Zeitungsartikel
Author(s):
Sahin, Özge and Czado, Claudia
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...     »
Keywords:
Clustering, copula, dependence, mixture model, non-Gaussian, vine copula
Dewey Decimal Classification:
510 Mathematik
Journal title:
Preprint
Year:
2021
Year / month:
2021-02
Quarter:
1. Quartal
Month:
Feb
WWW:
Arxiv
Status:
Preprint / submitted
TUM Institution:
Professur für Angewandte Mathematische Statistik
Format:
Text
 BibTeX