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Activity Number:
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69
- Longitudinal/Correlated Data II
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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Abstract #330339
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Presentation
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Title:
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Model Based Clustering via Copula and Applications
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Author(s):
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Marta Nai Ruscone*
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Companies:
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LIUC
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Keywords:
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Finite Mixtures;
Copula ;
Tail Dependence;
Multivariate Data;
Dependence
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Abstract:
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Finite mixtures are applied to perform model-based clustering of multivariate data. Existing models do not offer great flexibility for modelling the dependence of multivariate data since they rely on potentially undesirable correlation restrictions to be computationally tractable. Here, we propose a model-based clustering method via pair copula to reveal and fully understand the complex and hidden dependence patterns in correlated multivariate data. Since this approach is based on pair copula constructions it takes into account also tail asymmetry of the data by using blocks of asymmetric bivariate copulas We use simulated and real datasets to illustrate the proposed procedure.
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Authors who are presenting talks have a * after their name.
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