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Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Sahin, Özge; Czado, Claudia
Titel:
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...    »
Stichworte:
Dependence, ECM algorithm, model-based clustering, multivariate finite mixtures, pair-copula, statistical learning
Dewey Dezimalklassifikation:
510 Mathematik
Zeitschriftentitel:
Econometrics and Statistics
Jahr:
2022
Band / Volume:
22
Jahr / Monat:
2022-04
Quartal:
2. Quartal
Monat:
Apr
Seitenangaben Beitrag:
136-158
Sprache:
en
Volltext / DOI:
doi:10.1016/j.ecosta.2021.08.011
Verlag / Institution:
Elsevier BV
E-ISSN:
2452-3062
Publikationsdatum:
01.04.2022
TUM Einrichtung:
Professur für Angewandte Mathematische Statistik
Format:
Text
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