Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1145145 | Journal of Multivariate Analysis | 2016 | 21 Pages |
Abstract
This paper explores the theoretical properties and the practical usefulness of the general family of chi-square copulas that recently appeared in the literature. This class of dependence structures is very attractive, as it generalizes the Gaussian copula and allows for flexible modeling for high-dimensional random vectors. On one hand, expressions for the copula and the density in the bivariate and the multivariate case are derived and many theoretical properties are investigated, including expressions for popular measures of dependence, levels of asymmetry and constraints on the Kendall’s tau matrix. On the other hand, two applications of the chi-square copulas are developed, namely parameter estimation and spatial interpolation.
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Jean-François Quessy, Louis-Paul Rivest, Marie-Hélène Toupin,