Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1147916 | Journal of Statistical Planning and Inference | 2009 | 13 Pages |
Abstract
A new family of copulas is introduced that provides 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.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Aristidis K. Nikoloulopoulos, Dimitris Karlis,