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：我们用斯坦(Stein)的方法来为非线性学生统计数据建立统一的Berry-Esseen界限,强调可变审查以及一系列受审查变量的指数性随机集中不平等,以此作为包含相关论据的基本工具。 作为一个重要的应用,我们证明一个统一的Berry-Esseen(Berry-Esseen)为具有任何一定程度的内核的被学生化的U- Statistic提供统一的Berry-Esseen(Berry-Esseen ) 。