For two decades, reproducing kernels and their associated discrepancies have facilitated elegant theoretical analyses in the setting of quasi Monte Carlo. These same tools are now receiving interest in statistics and related fields, as criteria that can be used to select an appropriate statistical model for a given dataset. The focus of this article is on minimum kernel discrepancy estimators, whose use in statistical applications is reviewed, and a general theoretical framework for establishing their asymptotic properties is presented.

翻译：二十年来，再生核及其相关差异已在准蒙特卡洛设置中为优雅的理论分析提供了便利。这些相同的工具如今在统计学及相关领域也受到关注，可作为选择适用于给定数据集的统计模型的标准。本文聚焦于最小核差异估计量，综述其在统计应用中的使用，并提出了一个用于建立其渐近性质的通用理论框架。