Explaining the predictions of deep neural nets has been a topic of great interest in the
computer vision literature. While several gradient-based interpretation schemes have been …

First-order covariance inequalities via Stein's method Page 1 Bernoulli 26(3), 2020, 2051–2081
https://doi.org/10.3150/19-BEJ1182 First-order covariance inequalities via Stein’s method …

This paper generalizes Stein's Lemma recently obtained for elliptical class distributions to
the generalized skew-elliptical family of distributions. Stein's Lemma provides a useful tool …

We study a novel class of affine‐invariant and consistent tests for multivariate normality. The
tests are based on a characterization of the standard d‐variate normal distribution by way of …

In this paper we present a general framework for Stein's method for multivariate continuous
distributions. The approach gives a collection of Stein characterisations, among which we …

Inspired by Stein's lemma, we derive two expressions for the joint moments of elliptical
distributions. We use two different methods to derive E [X 1 2 f (X)] for any measurable …

Some Stein-type inequalities for multivariate elliptical distributions and applications -
ScienceDirect Skip to main contentSkip to article Elsevier logo Journals & Books Search …

This note consists of two parts. In the first part, we provide a pedagogic review on the
multivariate generalized hyperbolic (MGH) distribution. We show that this probability family …

When two variables are bivariate normally distributed, Stein's (1973, 1981) seminal lemma
provides a convenient expression for the covariance of the first variable with a function of the …

In this letter we derive the multivariate Stein's lemma for truncated elliptical random vectors.
The results in this letter generalize Stein's lemma for elliptical random vectors given in …