New Edgeworth-type expansions with finite sample guarantees
The Annals of Statistics · 2022 · Volume 50 · Issue 5
Abstract
We establish higher-order nonasymptotic expansions for a difference between probability distributions of sums of i.i.d. random vectors in a Euclidean space. The derived bounds are uniform over two classes of sets: the set of all Euclidean balls and the set of all half-spaces. These results allow to account for an impact of higher-order moments or cumulants of the considered distributions; the obtained error terms depend on a sample size and a dimension explicitly. The new inequalities outperform accuracy of the normal approximation in existing Berry–Esseen inequalities under very general conditions. Under some symmetry assumptions on the probability distribution of random summands, the obtained results are optimal in terms of the ratio between the dimension and the sample size. The new technique which we developed for establishing nonasymptotic higher-order expansions can be interesting by itself. Using the new higher-order inequalities, we study accuracy of the nonparametric bootstrap approximation and propose a bootstrap score test under possible model misspecification. The results of the paper also include explicit error bounds for general elliptic confidence regions for an expected value of the random summands, and optimality of the Gaussian anticoncentration inequality over the set of all Euclidean balls.
Keywords
62E17, 62F25, 62F40, anticoncentration inequality, bootstrap, bootstrap score test, chi-square approximation, dependence on dimension, Edgeworth series, elliptic confidence sets, finite sample inference, higher-order accuracy, linear contrasts, model misspecification, Multivariate Berry–Esseen inequality
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