Multivariate approximation in total variation using local dependence

Abstract

We establish two theorems for assessing the accuracy in total variation of multivariate discrete normal approximation to the distribution of an integer valued random vector W. The first is for sums of random vectors whose dependence structure is local. The second applies to random vectors W resulting from integrating the ℤd-valued marks of a marked point process with respect to its ground process. The error bounds are of magnitude comparable to those given in [Rinott & Rotar (1996)], but now with respect to the stronger total variation distance. Instead of requiring the summands to be bounded, we make third moment assumptions. We demonstrate the use of the theorems in four applications: Mono..