论文标题
最小内核差异估计器
Minimum Kernel Discrepancy Estimators
论文作者
论文摘要
二十年来，繁殖核及其相关的差异在准蒙特卡洛的环境中促进了优雅的理论分析。这些相同的工具现在正在收到对统计和相关字段的兴趣，作为可用于选择给定数据集的适当统计模型的标准。本文的重点是对最小内核差异估计量进行，其在统计应用中的使用进行了审查，并提出了建立其渐近性能的一般理论框架。
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.
