AbstractThe supermodular and the symmetric supermodular stochastic orders have been cursorily studied in previous literature. In this paper we study these orders more thoroughly. First we obtain some basic properties of these orders. We then apply these results in order to obtain comparisons of random vectors with common values, but with different levels of multiplicity. Specifically, we show that if the vectors of the levels of multiplicity are ordered in the majorization order, then the associated random vectors are ordered in the symmetric supermodular stochastic order. In the non-symmetric case we obtain bounds (in the supermodular stochastic order sense) on such random vectors. Finally, we apply the results to problems of optimal assem...
AbstractEvery univariate random variable is smaller, with respect to the ordinary stochastic order a...
Key words and phrases: multivariate random sums, multivariate stochastic orders, convex order, direc...
AbstractA function f(x) defined on X = X1 × X2 × … × Xn where each Xi is totally ordered satisfying ...
AbstractThe supermodular and the symmetric supermodular stochastic orders have been cursorily studie...
Consider random vectors formed by a finite number of independent groups of i.i.d. random variables, ...
In many economic applications involving comparisons of multivariate distributions, supermodularity o...
This paper uses the stochastic dominance approach to study orderings of inter-dependence for n-dimen...
AbstractIn this paper we solve two open problems posed by Joe (1997) concerning the supermodular ord...
International audienceIn this paper we solve two open problems posed by Joe (1997) concerning the su...
Consider random vectors formed by a finite number of independent groups of i.i.d.\ random variables,...
In this paper we solve two open problems posed by Joe (1997) concerning the supermodular order. Firs...
The supermodular order is a well-known tool to compare the intrinsic degree of dependence between r...
AbstractIn this paper, we show that a vector of positively/negatively associated random variables is...
In this paper we extend some recent results on the comparison of multivariate risk vectors w.r.t. su...
The supermodular order on multivariate distributions has many applications in financial and actuaria...
AbstractEvery univariate random variable is smaller, with respect to the ordinary stochastic order a...
Key words and phrases: multivariate random sums, multivariate stochastic orders, convex order, direc...
AbstractA function f(x) defined on X = X1 × X2 × … × Xn where each Xi is totally ordered satisfying ...
AbstractThe supermodular and the symmetric supermodular stochastic orders have been cursorily studie...
Consider random vectors formed by a finite number of independent groups of i.i.d. random variables, ...
In many economic applications involving comparisons of multivariate distributions, supermodularity o...
This paper uses the stochastic dominance approach to study orderings of inter-dependence for n-dimen...
AbstractIn this paper we solve two open problems posed by Joe (1997) concerning the supermodular ord...
International audienceIn this paper we solve two open problems posed by Joe (1997) concerning the su...
Consider random vectors formed by a finite number of independent groups of i.i.d.\ random variables,...
In this paper we solve two open problems posed by Joe (1997) concerning the supermodular order. Firs...
The supermodular order is a well-known tool to compare the intrinsic degree of dependence between r...
AbstractIn this paper, we show that a vector of positively/negatively associated random variables is...
In this paper we extend some recent results on the comparison of multivariate risk vectors w.r.t. su...
The supermodular order on multivariate distributions has many applications in financial and actuaria...
AbstractEvery univariate random variable is smaller, with respect to the ordinary stochastic order a...
Key words and phrases: multivariate random sums, multivariate stochastic orders, convex order, direc...
AbstractA function f(x) defined on X = X1 × X2 × … × Xn where each Xi is totally ordered satisfying ...