The bivariate K-finite normal mixture 'blanket' copula

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Nikoloulopoulos, Aristidis K. ORCID: https://orcid.org/0000-0003-0853-0084 (2022) The bivariate K-finite normal mixture 'blanket' copula. Journal of Statistical Computation and Simulation, 92 (6). pp. 1224-1245. ISSN 0094-9655

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Abstract

There exist many bivariate parametric copulas to model bivariate data with different dependence features. We propose a new bivariate parametric copula family that cannot only handle various dependence patterns that appear in the existing parametric bivariate copula families, but also provides a more enriched dependence structure. The proposed copula construction exploits finite mixtures of bivariate normal distributions. The mixing operation, the distinct correlation and mean parameters at each mixture component introduce quite a flexible dependence. The new parametric copula is theoretically investigated, compared with a set of classical bivariate parametric copulas and illustrated on two empirical examples from astrophysics and agriculture where some of the variables have peculiar and asymmetric dependence, respectively.

Item Type:	Article
Uncontrolled Keywords:	bivariate copulas,kullback–leibler distance,dependence structure,mixtures of bivariate normal distributions,statistics and probability,modelling and simulation,statistics, probability and uncertainty,applied mathematics,3* ,/dk/atira/pure/subjectarea/asjc/2600/2613
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Statistics (former - to 2024) Faculty of Science > Research Groups > Numerical Simulation, Statistics & Data Science
Related URLs:	http://www.scopus.com/inward/record.url?... https://arxiv.org/abs/1911.12284
Depositing User:	LivePure Connector
Date Deposited:	06 Oct 2021 01:59
Last Modified:	07 Nov 2024 12:44
URI:	https://ueaeprints.uea.ac.uk/id/eprint/81565
DOI:	10.1080/00949655.2021.1990292

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