A Maximal Large Deviation Inequality for Sub-Gaussian Variables

Details A Maximal Large Deviation Inequality for Sub-Gaussian Variables A Maximal Large Deviation Inequality for Sub-Gaussian Variables

2011-05-12

arxiv.org/abs/1105.2550v3

Computer Science

Learning

This paper has been withdrawn by the authors due to a crucial error in the last sentence of the proof of Theorem 1: "we can ta

Scientific paper

In this short note we prove a maximal concentration lemma for sub-Gaussian random variables stating that for independent sub-Gaussian random variables we have \[P<(\max_{1\le i\le N}S_{i}>\epsilon>) \le\exp<(-\frac{1}{N^2}\sum_{i=1}^{N}\frac{\epsilon^{2}}{2\sigma_{i}^{2}}>), \] where $S_i$ is the sum of $i$ zero mean independent sub-Gaussian random variables and $\sigma_i$ is the variance of the $i$th random variable.

Affiliated with

Also associated with

No associations

Rating

A Maximal Large Deviation Inequality for Sub-Gaussian Variables does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with A Maximal Large Deviation Inequality for Sub-Gaussian Variables, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A Maximal Large Deviation Inequality for Sub-Gaussian Variables will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-497568