Constructing multivariate distributions with specific marginal distributions
Journal of Multivariate Analysis
Asymptotic efficiency of the two-stage estimation method for copula-based models
Journal of Multivariate Analysis
Nonparametric rank-based tests of bivariate extreme-value dependence
Journal of Multivariate Analysis
Aggregation functions with stronger types of monotonicity
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Journal of Multivariate Analysis
Likelihood inference for Archimedean copulas in high dimensions under known margins
Journal of Multivariate Analysis
d-Dimensional dependence functions and Archimax copulas
Fuzzy Sets and Systems
Some results on a transformation of copulas and quasi-copulas
Information Sciences: an International Journal
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In this paper we introduce two methods for the construction of asymmetric multivariate copulas. The first is connected with products of copulas. The second approach generalises the Archimedean copulas. The resulting copulas are asymmetric and may have more than two parameters in contrast to most of the parametric families of copulas described in the literature. We study the properties of the proposed families of copulas such as the dependence of two components (Kendall's tau, tail dependence), marginal distributions and the generation of random variates.