On Stein's Method for Multivariate Self-Decomposable Laws With Finite First Moment

TL;DR

This paper extends Stein's method to multivariate self-decomposable distributions with finite moments, providing tools for quantitative approximation bounds and characterizations in higher dimensions.

Contribution

It introduces a multidimensional Stein methodology, Stein kernels, and discrepancy measures tailored for self-decomposable laws, advancing the analysis of infinitely divisible distributions.

Findings

Derived quantitative bounds on Wasserstein distances.

Introduced Stein kernels for self-decomposable laws.

Established existence results under spectral gap assumptions.

Abstract

We develop a multidimensional Stein methodology for non-degenerate self-decomposable random vectors in $\mathbb{R}^d$ having finite first moment. Building on previous univariate findings, we solve an integro-partial differential Stein equation by a mixture of semigroup and Fourier analytic methods. Then, under a second moment assumption, we introduce a notion of Stein kernel and an associated Stein discrepancy specifically designed for infinitely divisible distributions. Combining these new tools, we obtain quantitative bounds on smooth-Wasserstein distances between a probability measure in $\mathbb{R}^d$ and a non-degenerate self-decomposable target law with finite second moment. Finally, under an appropriate spectral gap assumption, we investigate, via variational methods, the existence of Stein kernels. In particular, this leads to quantitative versions of classical results on characterizations of probability distributions by variational functionals.