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
We consider the class of multivariate distributions that gives the distribution of the sum of uncorr...
AbstractA nonparametric test of the mutual independence between many numerical random vectors is pro...
AbstractIn the multivariate case, the empirical dependence function, defined as the empirical distri...
AbstractAccording to the celebrated Lukacs theorem, independence of quotient and sum of two independ...
AbstractThis paper adds to the numerous investigations of second order conditional structure of line...
The concept of sub-independence is based on the convolution of the distributions of the random varia...
AbstractLet Xj (j = 1,…,n) be i.i.d. random variables, and let Y′ = (Y1,…,Ym) and X′ = (X1,…,Xn) be ...
AbstractWe consider the class of multivariate distributions that gives the distribution of the sum o...
In this paper we will prove a characterization for the independence of random vectors with positive ...
AbstractIf W and Z are independent random vectors and Y1, Y2, …, Yn are the result of a transformati...
AbstractA method is given for testing the independence of variates in an infinitely divisible random...
AbstractIt is known that if the statistic Y = Σj=1n(Xj + aj)2 is drawn from a population which is di...
Conditional independence almost everywhere in the space of the conditioning variates does not imply ...
Conditional independence almost everywhere in the space of the conditioning variates does not imply ...
Abstract. Let {Xi, 1 ≤ i ≤ n} be a sequence of i.i.d. sequence of positive random variables with com...
We consider the class of multivariate distributions that gives the distribution of the sum of uncorr...
AbstractA nonparametric test of the mutual independence between many numerical random vectors is pro...
AbstractIn the multivariate case, the empirical dependence function, defined as the empirical distri...
AbstractAccording to the celebrated Lukacs theorem, independence of quotient and sum of two independ...
AbstractThis paper adds to the numerous investigations of second order conditional structure of line...
The concept of sub-independence is based on the convolution of the distributions of the random varia...
AbstractLet Xj (j = 1,…,n) be i.i.d. random variables, and let Y′ = (Y1,…,Ym) and X′ = (X1,…,Xn) be ...
AbstractWe consider the class of multivariate distributions that gives the distribution of the sum o...
In this paper we will prove a characterization for the independence of random vectors with positive ...
AbstractIf W and Z are independent random vectors and Y1, Y2, …, Yn are the result of a transformati...
AbstractA method is given for testing the independence of variates in an infinitely divisible random...
AbstractIt is known that if the statistic Y = Σj=1n(Xj + aj)2 is drawn from a population which is di...
Conditional independence almost everywhere in the space of the conditioning variates does not imply ...
Conditional independence almost everywhere in the space of the conditioning variates does not imply ...
Abstract. Let {Xi, 1 ≤ i ≤ n} be a sequence of i.i.d. sequence of positive random variables with com...
We consider the class of multivariate distributions that gives the distribution of the sum of uncorr...
AbstractA nonparametric test of the mutual independence between many numerical random vectors is pro...
AbstractIn the multivariate case, the empirical dependence function, defined as the empirical distri...