Gaussian Approximations for Maxima of Random Vectors under (2+iota)-th Moments
classification
🧮 math.ST
stat.TH
keywords
gaussianiotamomentsrandomundervectorsabstractalong
read the original abstract
We derive a Gaussian approximation result for the maximum of a sum of random vectors under $(2+\iota)$-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.
This paper has not been read by Pith yet.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.