# First order covariance inequalities via Stein's method

## TL;DR

This paper develops new covariance inequalities and Stein kernel expressions using Stein's method, providing explicit bounds and identities for univariate distributions with broad applications.

## Contribution

It introduces probabilistic representations for inverse Stein operators, leading to new covariance identities and bounds that generalize classical variance inequalities.

## Key findings

- Derived new simple expressions for Stein kernels.
- Established sharp covariance bounds using weighted identities.
- Connected results to classical variance bounds and recent literature.

## Abstract

We propose probabilistic representations for inverse Stein operators (i.e. solutions to Stein equations) under general conditions; in particular we deduce new simple expressions for the Stein kernel. These representations allow to deduce uniform and non-uniform Stein factors (i.e. bounds on solutions to Stein equations) and lead to new covariance identities expressing the covariance between arbitrary functionals of an arbitrary {univariate} target in terms of a weighted covariance of the derivatives of the functionals. Our weights are explicit, easily computable in most cases, and expressed in terms of objects familiar within the context of Stein's method. Applications of the Cauchy-Schwarz inequality to these weighted covariance identities lead to sharp upper and lower covariance bounds and, in particular, weighted Poincar\'e inequalities. Many examples are given and, in particular, classical variance bounds due to Klaassen, Brascamp and Lieb or Otto and Menz are corollaries. Connections with more recent literature are also detailed.