A multivariate version of Williamson's theorem, \(\ell^1\)-symmetric survival functions, and generalized Archimedean copulas
DOI10.1515/demo-2018-0020zbMath1434.62085OpenAlexW2907228157MaRDI QIDQ2283655
Publication date: 13 January 2020
Published in: Dependence Modeling (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1515/demo-2018-0020
Archimedean copulamultivariate survival functionWilliamson's theoremhigher order monotonicitymonotone composition theorem
Characterization and structure theory for multivariate probability distributions; copulas (62H05) Probability distributions: general theory (60E05) Monotonic functions, generalizations (26A48) Reliability and life testing (62N05) Representation and superposition of functions (26B40)
Related Items (4)
Cites Work
- Hierarchical Archimedean copulas through multivariate compound distributions
- Homogeneous distributions -- and a spectral representation of classical mean values and stable tail dependence functions
- Monotonicity properties of multivariate distribution and survival functions -- with an application to Lévy-frailty copulas
- Higher order monotonic functions of several variables
- Functions operating on multivariate distribution and survival functions - With applications to classical mean-values and to copulas
- Multiply monotone functions and their Laplace transforms
- Multivariate Archimedean copulas, \(d\)-monotone functions and \(\ell _{1}\)-norm symmetric distributions
- Copulas, stable tail dependence functions, and multivariate monotonicity
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