Abstract
In this article, we create a decomposition that represents and describes the depen-dence structure between two variables. Since copulas provide a deep understanding of the dependence structure by eliminating the effects of the marginals, they play a key role in this study. We define a discretized copula density matrix and decompose it into a set of permutation matrices by using the Birkhoff-von Neumann theorem. This decomposition provides a way to effectively apply the concepts of copulas to solve problems in multivariate statistical data analysis.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2269-2282 |
| Number of pages | 14 |
| Journal | Communications in Statistics - Theory and Methods |
| Volume | 34 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 2005 |
Keywords
- Birkhoff-von Neumann theorem
- Copulas
- Discretized copulas
- Doubly stochastic matrix
- Gini's measure
- Permutation matrix
- Prior probability