Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1145437 | Journal of Multivariate Analysis | 2015 | 14 Pages |
Abstract
Copulas are a useful tool to model multivariate distributions. While there exist various families of bivariate copulas, much less work has been done when the dimension is higher. We propose a class of multivariate copulas based on products of transformed bivariate copulas. The analytical forms of the copulas within this class allow to naturally associate a graphical structure which helps to visualize the dependencies and to compute the full joint likelihood even in high dimension. Numerical experiments are conducted both on simulated and real data thanks to a dedicated R package.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Gildas Mazo, Stéphane Girard, Florence Forbes,