A multivariate Berry--Esseen theorem with explicit constants

Abstract

We provide a Lyapunov type bound in the multivariate central limit theorem for sums of independent, but not necessarily identically distributed random vectors. The error in the normal approximation is estimated for certain classes of sets, which include the class of measurable convex sets. The error bound is stated with explicit constants. The result is proved by means of Stein's method. In addition, we improve the constant in the bound of the Gaussian perimeter of convex sets.

arXiv comment30 pages, 1 figure, 1 table

References in corpus (2)

• Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
• Stein's method in high dimensions with applications

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