Results 31 to 40 of about 97 (72)
Dependence Modeling, 2019 We study the dynamics of the family of copulas {Ct}t≥0 of a pair of stochastic processes given by stochastic differential equations (SDE). We associate to it a parabolic partial differential equation (PDE). Having embedded the set of bivariate copulas in
Jaworski Piotr
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On the lower bound of Spearman’s footrule
Dependence Modeling, 2019 Úbeda-Flores showed that the range of multivariate Spearman’s footrule for copulas of dimension d ≥ 2 is contained in the interval [−1/d, 1], that the upper bound is attained exclusively by the upper Fréchet-Hoeffding bound, and that the lower bound is ...
Fuchs Sebastian, McCord Yann
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, 1992 International Journal of Mathematics and Mathematical Sciences, Volume 16, Issue 1, Page 165-168, 1993.
Arjun K. Gupta +2 more
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Journal of Mathematics, Volume 2025, Issue 1, 2025.This article aims to present a new type‐II claims Pareto extension for statistical reliability and actuarial analysis. The new probabilistic density can be simplified in terms of the baseline densities. Some new bivariate types were developed under some copula approaches.
Atef F. Hashem +6 more
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On a class of norms generated by nonnegative integrable distributions
Dependence Modeling, 2019 We show that any distribution function on ℝd with nonnegative, nonzero and integrable marginal distributions can be characterized by a norm on ℝd+1, called F-norm. We characterize the set of F-norms and prove that pointwise convergence of a sequence of F-
Falk Michael, Stupfler Gilles
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Dependence Modeling, 2020 The use of the exponential distribution and its multivariate generalizations is extremely popular in lifetime modeling. Freund’s bivariate exponential model (1961) is based on the idea that the remaining lifetime of any entity in a bivariate system is ...
Guzmics Sándor, Pflug Georg Ch.
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Dependence Modeling, 2023 The probability integral transform of a continuous random variable XX with distribution function FX{F}_{X} is a uniformly distributed random variable U=FX(X)U={F}_{X}\left(X). We define the angular probability integral transform (APIT) as θU=2πU=2πFX(X){\
Fernández-Durán Juan José +1 more
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On Conditional Value at Risk (CoVaR) for tail-dependent copulas
Dependence Modeling, 2017 The paper deals with Conditional Value at Risk (CoVaR) for copulas with nontrivial tail dependence. We show that both in the standard and the modified settings, the tail dependence function determines the limiting properties of CoVaR as the conditioning ...
Jaworski Piotr
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On comprehensive families of copulas involving the three basic copulas and transformations thereof
Dependence ModelingComprehensive families of copulas including the three basic copulas (at least as limit cases) are useful tools to model countermonotonicity, independence, and comonotonicity of pairs of random variables on the same probability space. In this contribution,
Saminger-Platz Susanne +4 more
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A two-component copula with links to insurance
Dependence Modeling, 2017 This paper presents a new copula to model dependencies between insurance entities, by considering how insurance entities are affected by both macro and micro factors.
Ismail S., Yu G., Reinert G., Maynard T.
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