Optimal constants in concentration inequalities on the sphere and in the Gauss space

Fuente: arXiv
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Main Authors: Aubrun, Guillaume, Jenkinson, Justin, Szarek, Stanislaw J.
Format: Preprint
Published: 2024
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author Aubrun, Guillaume
Jenkinson, Justin
Szarek, Stanislaw J.
author_facet Aubrun, Guillaume
Jenkinson, Justin
Szarek, Stanislaw J.
contents We show several variants of concentration inequalities on the sphere stated as subgaussian estimates with optimal constants. For a Lipschitz function, we give one-sided and two-sided bounds for deviation from the median as well as from the mean. For example, we show that if $μ$ is the normalized surface measure on $S^{n-1}$ with $n\geq 3$, $f : S^{n-1} \to \mathbb{R}$ is $1$-Lipschitz, $M$ is the median of $f$, and $t >0$, then $μ\big(f \geq M +t\big) \leq \frac 12 e^{-nt^2/2}$. If $M$ is the mean of $f$, we have a two-sided bound $μ\big(|f - M| \geq t\big) \leq e^{-nt^2/2}$. Consequently, if $γ$ is the standard Gaussian measure on $\mathbb{R}^n$ and $f : \mathbb{R}^{n} \to \mathbb{R}$ (again, $1$-Lipschitz, with the mean equal to $M$), then $γ\big(|f - M| \geq t\big) \leq e^{-t^2/2}$. These bounds are slightly better and arguably more elegant than those available elsewhere in the literature.
format Preprint
id arxiv_https___arxiv_org_abs_2406_13581
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Optimal constants in concentration inequalities on the sphere and in the Gauss space
Aubrun, Guillaume
Jenkinson, Justin
Szarek, Stanislaw J.
Probability
Functional Analysis
46B, 60B, 60E
We show several variants of concentration inequalities on the sphere stated as subgaussian estimates with optimal constants. For a Lipschitz function, we give one-sided and two-sided bounds for deviation from the median as well as from the mean. For example, we show that if $μ$ is the normalized surface measure on $S^{n-1}$ with $n\geq 3$, $f : S^{n-1} \to \mathbb{R}$ is $1$-Lipschitz, $M$ is the median of $f$, and $t >0$, then $μ\big(f \geq M +t\big) \leq \frac 12 e^{-nt^2/2}$. If $M$ is the mean of $f$, we have a two-sided bound $μ\big(|f - M| \geq t\big) \leq e^{-nt^2/2}$. Consequently, if $γ$ is the standard Gaussian measure on $\mathbb{R}^n$ and $f : \mathbb{R}^{n} \to \mathbb{R}$ (again, $1$-Lipschitz, with the mean equal to $M$), then $γ\big(|f - M| \geq t\big) \leq e^{-t^2/2}$. These bounds are slightly better and arguably more elegant than those available elsewhere in the literature.
title Optimal constants in concentration inequalities on the sphere and in the Gauss space
topic Probability
Functional Analysis
46B, 60B, 60E
url https://arxiv.org/abs/2406.13581