New representations of Stein operators produce explicit weighted covariance identities that deliver sharp upper and lower covariance bounds and weighted Poincaré inequalities for univariate targets, recovering classical results as corollaries.
On Stein's Method for Multivariate Self-Decomposable Laws With Finite First Moment
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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.
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math.PR 1years
2019 1verdicts
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First order covariance inequalities via Stein's method
New representations of Stein operators produce explicit weighted covariance identities that deliver sharp upper and lower covariance bounds and weighted Poincaré inequalities for univariate targets, recovering classical results as corollaries.