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Multivariate normal approximations by Stein's method and size bias couplings

1996
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Journal of Applied Probability
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Stein's method is used to obtain two theorems on multivariate normal approximation. Our main theorem, Theorem 1.2, provides a bound on the distance to normality for any non-negative random vector. Theorem 1.2 requires multivariate size bias coupling, which we discuss in studying the approximation of distributions of sums of dependent random vectors. In the univariate case, we briefly illustrate this approach for certain sums of nonlinear functions of multivariate normal variables. As a second

doi:10.2307/3215259
fatcat:gl3jprudbffefkzwckjrk5dvbe