We discuss a two-dimensional analog of the probability integral transform for bivariate distribution functions H1 and H2, i.e., the distribution function of the random variable H1(X; Y ) given that the joint distribution function of the random variables X and Y is H2. We study the case when H1 and H
Distribution functions of multivariate copulas
✍ Scribed by José A. Rodrı́guez-Lallena; Manuel Úbeda-Flores
- Publisher
- Elsevier Science
- Year
- 2003
- Tongue
- English
- Weight
- 239 KB
- Volume
- 64
- Category
- Article
- ISSN
- 0167-7152
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✦ Synopsis
For continuous random vectors X = (X 1 ; X 2 ; : : : ; X n ) and multivariate distribution functions H 1 and H 2 with common univariate marginals, we study the distribution function of the random variable H 1 (X) given that the joint distribution function of X is H 2 . We show that the distribution function of H 1 (X) depends only on the copulas C 1 and C 2 associated with H 1 and H 2 , and examine various properties of these distribution functions. We also illustrate some applications including multivariate dependence orderings.
📜 SIMILAR VOLUMES
In most situations, the dependence monotonicities of copulas are checked by problem-speciÿc approaches. Sometimes, it is impossible to check the monotonicities from the analytic forms of copulas. The purpose of this paper is to lay out some general results that can be used to identify dependence par