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Document type:
Zeitschriftenaufsatz 
Author(s):
Sahin, Özge; 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 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 fo...    »
 
Keywords:
Dependence, ECM algorithm, model-based clustering, multivariate finite mixtures, pair-copula, statistical learning 
Dewey Decimal Classification:
510 Mathematik 
Journal title:
Econometrics and Statistics 
Year:
2022 
Journal volume:
22 
Year / month:
2022-04 
Quarter:
2. Quartal 
Month:
Apr 
Pages contribution:
136-158 
Language:
en 
Publisher:
Elsevier BV 
E-ISSN:
2452-3062 
Date of publication:
01.04.2022 
TUM Institution:
Professur für Angewandte Mathematische Statistik 
Format:
Text