Minimum Kernel Discrepancy Estimators
Methodology
2023-08-24 v2 Statistics Theory
Statistics Theory
Abstract
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.
Cite
@article{arxiv.2210.16357,
title = {Minimum Kernel Discrepancy Estimators},
author = {Chris. J. Oates},
journal= {arXiv preprint arXiv:2210.16357},
year = {2023}
}
Comments
To appear in: A. Hinrichs, P. Kritzer, F. Pillichshammer (eds.). Monte Carlo and Quasi-Monte Carlo Methods 2022. Springer Verlag