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Document type:
Zeitungsartikel 
Author(s):
Sahina, Özge and Czado, Claudia 
Title:
Vine copula mixture models and clustering for non-Gaussian data 
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:
Status:
Preprint / submitted 
TUM Institution:
Professur für Angewandte Mathematische Statistik 
Format:
Text