Results 61 to 70 of about 355 (84)
, 2014 Many developments in Mathematics involve the computation of higher order derivatives of Gaussian density functions. The analysis of univariate Gaussian random variables is a well-established field whereas the analysis of their multivariate counterparts ...
Chacón, José E., Duong, Tarn
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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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The deFinetti representation of generalised Marshall–Olkin sequences
Dependence Modeling, 2020 We show that each infinite exchangeable sequence τ1, τ2, . . . of random variables of the generalised Marshall–Olkin kind can be uniquely linked to an additive subordinator via its deFinetti representation. This is useful for simulation, model estimation,
Sloot Henrik
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Bivariate copulas defined from matrices [PDF]
, 2013 We propose a semiparametric family of copulas based on a set of orthonormal functions and a matrix. This new copula permits to reach values of Spearman's Rho arbitrarily close to one without introducing a singular component.
Amblard, Cécile +2 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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Discovering a junction tree behind a Markov network by a greedy algorithm
, 2011 In an earlier paper we introduced a special kind of k-width junction tree, called k-th order t-cherry junction tree in order to approximate a joint probability distribution.
A Altmüller +27 more
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Depth functions based on a number of observations of a random vector [PDF]
We present two statistical depth functions given in terms of the random variable defined as the minimum number of observations of a random vector that are needed to include a fixed given point in their convex hull.
Ignacio Cascos
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Price majorization and the inverse Lorenz function [PDF]
The paper presents an approach to the measurement of economic disparity in several commodities. We introduce a special view on the usual Lorenz curve and extend this view to the multivariate situation: Given a vector of shares of the total endowments in ...
Koshevoy, Gleb, Mosler, Karl
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On quantile based co-risk measures and their estimation
Dependence Modeling, 2020 Conditional Value-at-Risk (CoVaR) is defined as the Value-at-Risk of a certain risk given that the related risk equals a given threshold (CoVaR=) or is smaller/larger than a given threshold ...
Fuchs Sebastian, Trutschnig Wolfgang
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On Multivariate Hyperbolically Completely Monotone Densities and Their Laplace Transforms
, 2015 The class HCM consists of all nonnegative functions f such that f(uv)*f(u/v)is completely monotone with respect to w=v+1/v, for all fixed positive numbers u, and has been extensively studied for a long time.
Sjödin, Tord
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