Inequalities for stochastic models via supermodular orderings
Inequalities for stochastic models via supermodular orderings
复制标题
DOI：
10.1080/15326349708807420
复制
发表时间：
1997
影响因子：
0.7
通讯作者：
N. Bäuerle
中科院分区：
文献类型：
--
作者：
N. Bäuerle
本文的目的是利用超模排序得到随机向量的不等式。这种排序的性质表明，可以将其用作随机向量中的“依赖强度”的比较。与已经建立的这种类型的排序相比，超模排序的优点是不需要假定比较中的随机向量的公共边缘分布。将其应用于多元正态分布、马尔可夫链和一些随机模型，得到了新的不等式
The aim of this paper is to derive inequalities for random vectors by using the supermodular ordering. The properties of this ordering suggest to use it as a comparison for the “ strength of dependence” in random vectors. In contrast to already established orderings of this type, the supermodular ordering has the advantage that it is not necessary to assume a common marginal distribution for the random vectors under comparison. As a consequence we obtain new inequalities by applying it to multivariate normal distributions, Markov chains and some stochastic models