Finite normal mixture copulas for multivariate discrete data modeling. (English) Zbl 1169.62044
Summary: A new family of copulas is introduced that provides a flexible dependence structure while being tractable and simple to use for multivariate discrete data modeling. The construction exploits finite mixtures of uncorrelated normal distributions. Accordingly, the cumulative distribution function is simply the product of univariate normal distributions. At the same time, however, the mixing operation introduces association. The properties of the new family of copulas are examined and a concrete application is used to show its applicability.
MSC:
| 62H05 | Characterization and structure theory for multivariate probability distributions; copulas |
| 62H10 | Multivariate distribution of statistics |
| 62H20 | Measures of association (correlation, canonical correlation, etc.) |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
Keywords:
copula; count data; discrete distribution; finite normal mixture; negative binomial distribution; Kendall’s tau; epidemiologyReferences:
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