Central limit theorem and bootstrap approximation in high dimensions: Near 1/n rates via implicit smoothing
针对p维独立同分布随机向量和，研究了高斯逼近和自助法逼近的Berry-Esseen界，在子高斯或子指数条件下实现了对n的近n^{-1/2}依赖，证明使用了Lindeberg插值中的隐式平滑操作。
Nonasymptotic bounds for Gaussian and bootstrap approximation have recently attracted significant interest in high-dimensional statistics. This paper studies Berry–Esseen bounds for such approximations with respect to the multivariate Kolmogorov distance, in the context of a sum of n random vectors that are p-dimensional and i.i.d. Up to now, a growing line of work has established bounds with mild logarithmic dependence on p. However, the problem of developing corresponding bounds with near n−1/2 dependence on n has remained largely unresolved. Within the setting of random vectors that have sub-Gaussian or subexponential entries, this paper establishes bounds with near n−1/2 dependence, for both Gaussian and bootstrap approximation. In addition, the proofs are considerably distinct from other recent approaches, and make use of an “implicit smoothing” operation in the Lindeberg interpolation.