Results 41 to 50 of about 71 (62)
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
doaj +1 more source
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.
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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.
doaj +1 more source
Nonparametric C- and D-vine-based quantile regression
Dependence Modeling, 2022 Quantile regression is a field with steadily growing importance in statistical modeling. It is a complementary method to linear regression, since computing a range of conditional quantile functions provides more accurate modeling of the stochastic ...
Tepegjozova Marija +3 more
doaj +1 more source
Copula modeling for discrete random vectors
Dependence Modeling, 2020 Copulas have now become ubiquitous statistical tools for describing, analysing and modelling dependence between random variables. Sklar’s theorem, “the fundamental theorem of copulas”, makes a clear distinction between the continuous case and the ...
Geenens Gery
doaj +1 more source
Time series with infinite-order partial copula dependence
Dependence Modeling, 2022 Stationary and ergodic time series can be constructed using an s-vine decomposition based on sets of bivariate copula functions. The extension of such processes to infinite copula sequences is considered and shown to yield a rich class of models that ...
Bladt Martin, McNeil Alexander J.
doaj +1 more source
The de Finetti structure behind some norm-symmetric multivariate densities with exponential decay
Dependence Modeling, 2020 We derive a sufficient condition on the symmetric norm ||·|| such that the probability distribution associated with the density function f (x) ∝exp(−λ ||x||) is conditionally independent and identically distributed in the sense of de Finetti’s seminal ...
Mai Jan-Frederik
doaj +1 more source