Sharp Gaussian Asymptotics for Marginals of Euclidean Balls

Source abstract

We study Gaussian approximation for probability measures obtained by normalizing one-dimensional profile functions, with one-dimensional marginals of Euclidean balls as the principal example. We first establish quantitative concentration estimates near the set of maximizers. For profiles with a unique nondegenerate maximizer, we give sufficient conditions under which centering at the maximizer and rescaling according to the local quadratic approximation of the logarithm of the profile yield densities that converge in $L^1(\mathbb{R})$ to the standard Gaussian density. We then specialize to one-dimensional marginals of Euclidean balls in $\mathbb{R}^n$. Writing $N=n-1$, we consider two standardizations of the marginal distribution: one determined by the logarithmic curvature at the maximizer, and the other by the exact standard deviation. For each standardization, we identify the first-order correction, of order $N^{-1}$, to the standard Gaussian density in $L^1(\mathbb{R})$. These expansions also determine the corresponding first-order corrections to the probabilities of symmetric intervals and the leading terms of the total variation distances from the standard Gaussian distribution. In particular, under exact-variance standardization, the total variation distance is asymptotic to an explicit positive constant times $N^{-1}$. Consequently, the previously known $O(N^{-1})$ bound for approximation by the Gaussian distribution with the same variance is sharp in order.