We take another look at using Stein's method to establish uniform Berry-Esseen bounds for Studentized nonlinear statistics, highlighting variable censoring and an exponential randomized concentration inequality for a sum of censored variables as the essential tools to carry the arguments involved. As an important application, we prove a uniform Berry-Esseen bound for Studentized U-statistics with a kernel of any given degree.

翻译：我们重新审视使用Stein方法建立Studentized非线性统计量的一致Berry-Esseen界，重点强调变量截断与截断变量和的指数型随机化集中不等式作为承载相关论证的核心工具。作为一项重要应用，我们证明了任意给定阶核的Studentized U-统计量的一致Berry-Esseen界。