The bivariate K-finite normal mixture 'blanket' copula

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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.
Original languageEnglish
Pages (from-to)1224-1245
Number of pages22
JournalJournal of Statistical Computation and Simulation
Volume92
Issue number6
Early online date21 Oct 2021
DOIs
Publication statusPublished - 2022

Keywords

  • Bivariate copulas
  • Kullback–Leibler distance
  • dependence structure
  • mixtures of bivariate normal distributions

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