Results 11 to 20 of about 4,162 (190)
Using Copulas to Model Dependence Between Crude Oil Prices of West Texas Intermediate and Brent-Europe [PDF]
Sahand Communications in Mathematical Analysis, 2020 In this study the main endeavor is to model dependence structure between crude oil prices of West Texas Intermediate (WTI) and Brent - Europe. The main activity is on concentrating copula technique which is powerful technique in modeling dependence ...
Vadoud Najjari
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On convergence of associative copulas and related results
Dependence Modeling, 2021 Triggered by a recent article establishing the surprising result that within the class of bivariate Archimedean copulas 𝒞ar different notions of convergence - standard uniform convergence, convergence with respect to the metric D1, and so-called weak ...
Kasper Thimo M. +2 more
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CoRR, 2020 A central problem in machine learning and statistics is to model joint densities of random variables from data. Copulas are joint cumulative distribution functions with uniform marginal distributions and are used to capture interdependencies in isolation from marginals.
Chun Kai Ling +2 more
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A Mixture of Clayton, Gumbel, and Frank Copulas: A Complete Dependence Model
Journal of Probability and Statistics, 2022 Knowledge of the dependence between random variables is necessary in the area of risk assessment and evaluation. Some of the existing Archimedean copulas, namely the Clayton and the Gumbel copulas, allow for higher correlations on the extreme left and ...
M. A. Boateng +3 more
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Extensions of Two Bivariate Strict Archimedean Copulas
Computational Journal of Mathematical and Statistical Sciences, 2023 The copula approach provides an option for capturing the structure of dependence between two quantitative variables. This approach is based on special bivariate functions called copulas.
Christophe Chesneau
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Archimedean copulae and positive dependence [PDF]
Journal of Multivariate Analysis, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
MUELLER A, SCARSINI, MARCO
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Results in Control and Optimization, 2023 In this paper, we study the convexity of the linear joint chance constraints. We assume that the constraint row vectors are elliptically distributed. Further, the dependence of the rows is modeled by a family of Archimedean copulas, namely, the Gumbel ...
Hoang Nam Nguyen, Abdel Lisser, Jia Liu
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International Journal of Statistics and Probability, 2021 The copula function is an effective and elegant tool useful for modeling dependence between random variables. Among the many families of this function, one of the most prominent family of copula is the Archimedean family, which has its unique structure and features.
Moshe Kelner +2 more
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General Multivariate Dependence using Associated Copulas
Revstat Statistical Journal, 2016 This paper studies the general multivariate dependence and tail dependence of a random vector. We analyse the dependence of variables going up or down, covering the 2 d orthants of dimension d and accounting for non-positive dependence.
Yuri Salazar Flores
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Hierarchical Archimedean Copulas for MATLAB and Octave: The HACopula Toolbox
Journal of Statistical Software, 2020 To extend the current implementation of copulas in MATLAB to non-elliptical distributions in arbitrary dimensions enabling for asymmetries in the tails, the toolbox HACopula provides functionality for modeling with hierarchical (or nested) Archimedean ...
Jan Górecki +2 more
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