AbstractAccording to the celebrated Lukacs theorem, independence of quotient and sum of two independent positive random variables characterizes the gamma distribution. Rather unexpectedly, it appears that in the multivariate setting, the analogous independence condition does not characterize the multivariate gamma distribution in general, but is far more restrictive: it implies that the respective random vectors have independent or linearly dependent components. Our basic tool is a solution of a related functional equation of a quite general nature. As a side effect the form of the multivariate distribution with univariate Pareto conditionals is derived
AbstractA discrete multivariate probability distribution for dependent random variables, which conta...
A multivariate probability model possessing a dependence structure that is reflected in its variance...
A discrete multivariate probability distribution for dependent random variables, which contains the ...
AbstractAccording to the celebrated Lukacs theorem, independence of quotient and sum of two independ...
In this paper we will prove a characterization for the independence of random vectors with positive ...
AbstractThis paper adds to the numerous investigations of second order conditional structure of line...
Copyright © 2014 Werner Hürlimann. This is an open access article distributed under the Creative Com...
Abstract. Let {Xi, 1 ≤ i ≤ n} be a sequence of i.i.d. sequence of positive random variables with com...
The problem of testing mutual independence of p random vectors in a general setting where the dimens...
AbstractThis paper adds to the numerous investigations of second order conditional structure of line...
A discrete multivariate probability distribution for dependent random variables, which contains the ...
AbstractA new nonparametric approach to the problem of testing the joint independence of two or more...
AbstractThe investigation of multivariate generalized Pareto distributions (GPDs) has begun only rec...
Consider a random vector (X,Y) where X=(X1,X2, ...., Xs,) and Y=(Y1,Y2, ...., Ys) with Xi, Yi, i=1,...
International audienceThis paper proposes a semi-parametric test of independence (or serial independ...
AbstractA discrete multivariate probability distribution for dependent random variables, which conta...
A multivariate probability model possessing a dependence structure that is reflected in its variance...
A discrete multivariate probability distribution for dependent random variables, which contains the ...
AbstractAccording to the celebrated Lukacs theorem, independence of quotient and sum of two independ...
In this paper we will prove a characterization for the independence of random vectors with positive ...
AbstractThis paper adds to the numerous investigations of second order conditional structure of line...
Copyright © 2014 Werner Hürlimann. This is an open access article distributed under the Creative Com...
Abstract. Let {Xi, 1 ≤ i ≤ n} be a sequence of i.i.d. sequence of positive random variables with com...
The problem of testing mutual independence of p random vectors in a general setting where the dimens...
AbstractThis paper adds to the numerous investigations of second order conditional structure of line...
A discrete multivariate probability distribution for dependent random variables, which contains the ...
AbstractA new nonparametric approach to the problem of testing the joint independence of two or more...
AbstractThe investigation of multivariate generalized Pareto distributions (GPDs) has begun only rec...
Consider a random vector (X,Y) where X=(X1,X2, ...., Xs,) and Y=(Y1,Y2, ...., Ys) with Xi, Yi, i=1,...
International audienceThis paper proposes a semi-parametric test of independence (or serial independ...
AbstractA discrete multivariate probability distribution for dependent random variables, which conta...
A multivariate probability model possessing a dependence structure that is reflected in its variance...
A discrete multivariate probability distribution for dependent random variables, which contains the ...