A note on one-dimensional Poincar\'e inequalities by Stein-type integration

TL;DR

This paper introduces new bounds and iterative methods for estimating the weighted Poincaré constant of probability densities using Stein's method, with applications to various distributions.

Contribution

It develops a novel Stein-type variational formula for Poincaré constants and constructs sequences converging to these constants and their spectral solutions.

Findings

Derived simple bounds on Poincaré constants using Stein kernels

Constructed iterative sequences converging to the Poincaré constant and spectral solutions

Applied methods to diverse distributions like Gaussian, Beta, Gamma, and Weibull

Abstract

We study the weighted Poincar\'e constant $C(p,w)$ of a probability density $p$ with weight function $w$ using integration methods inspired by Stein's method. We obtain a new version of the Chen-Wang variational formula which, as a byproduct, yields simple upper and lower bounds on $C(p,w)$ in terms of the so-called Stein kernel of $p$. We also iterate these variational formulas so as to build sequences of nested intervals containing the Poincar\'e constant, sequences of functions converging to said constant, as well as sequences of functions converging to the solutions of the corresponding spectral problem. Our results rely on the properties of a pseudo inverse operator of the classical Sturm-Liouville operator. We illustrate our methods on a variety of examples: Gaussian functionals, weighted Gaussian, beta, gamma, Subbotin, and Weibull distributions.