Negatively Superadditive Dependence of Random Variables with Applications

A random vector X =（X₁,X₂,...,X_m） is said to be negatively superadditive dependent （NSD） if for every superadditive function ϕ, Eϕ（X₁, X₂,..., X_m）≤5 Eϕ（Y₁, Y₂,..,Y_m） where Y₁, Y₂,..,Y_m are independent with Y_i \stackreld= X_i for each i. Some basic properties and three structural theorems of NSD are derived and applied to show that a number of well-known multivariate distributions possess the NSD property. Applications are also presented.