Results 1 to 10 of about 119 (106)
, 1992 International Journal of Mathematics and Mathematical Sciences, Volume 16, Issue 1, Page 165-168, 1993.
Arjun K. Gupta +2 more
wiley +1 more source
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
wiley +1 more source
Discrete uniform mixtures via posterior means
, 1999 Beta-Pascal distribution, discrete uniform distribution, identification of mixtures, mixture, negative binomial distribution, negative hypergeometric distribution, posterior mean, primary 62H05, secondary 62F15,
Arjun K. Gupta +2 more
core +1 more source
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