Gaussian Approximations for Maxima of Random Vectors under $(2+\iota)$-th Moments
Statistics Theory
2019-05-28 v1 Statistics Theory
Abstract
We derive a Gaussian approximation result for the maximum of a sum of random vectors under -th moments. Our main theorem is abstract and nonasymptotic, and can be applied to a variety of statistical learning problems. The proof uses the Lindeberg telescopic sum device along with some other newly developed technical results.
Keywords
Cite
@article{arxiv.1905.11014,
title = {Gaussian Approximations for Maxima of Random Vectors under $(2+\iota)$-th Moments},
author = {Qiang Sun},
journal= {arXiv preprint arXiv:1905.11014},
year = {2019}
}
Comments
6 pages, short note