New normality test in high dimension with kernel methods
Statistics Theory
2014-04-14 v1 Statistics Theory
Abstract
A new goodness-of-fit test for normality in high-dimension (and Reproducing Kernel Hilbert Space) is proposed. It shares common ideas with the Maximum Mean Discrepancy (MMD) it outperforms both in terms of computation time and applicability to a wider range of data. Theoretical results are derived for the Type-I and Type-II errors. They guarantee the control of Type-I error at prescribed level and an exponentially fast decrease of the Type-II error. Synthetic and real data also illustrate the practical improvement allowed by our test compared with other leading approaches in high-dimensional settings.
Cite
@article{arxiv.1404.3188,
title = {New normality test in high dimension with kernel methods},
author = {Jérémie Kellner and Alain Celisse},
journal= {arXiv preprint arXiv:1404.3188},
year = {2014}
}