  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.
