Stein's method compares probability distributions through the study of a class of linear
operators called Stein operators. While mainly studied in probability and used to underpin …

This paper provides a general framework for Stein's density method for multivariate
continuous distributions. The approach associates to any probability density function a …

We propose a class of weighted L^ 2 L 2-type tests of fit to the Gamma distribution. Our novel
procedure is based on a fixed point property of a new transformation connected to a Steinian …

From the distributional characterizations that lie at the heart of Stein's method we derive
explicit formulae for the mass functions of discrete probability laws that identify those …

By extrapolating the explicit formula of the zero-bias distribution occurring in the context of
Stein's method, we construct characterization identities for a large class of absolutely …

Pareto distributions are widely used models in economics, finance and actuarial sciences.
As a result, a number of goodness-of-fit tests have been proposed for these distributions in …

The Stein-type identities are widely recognized for their substantial utility and potency in
deriving shrinkage estimators improving on crude estimators in normal, gamma, Poisson …

We propose a kernel-based nonparametric test of relative goodness of fit, where the goal is
to compare two models, both of which may have unobserved latent variables, such that the …

Abstract The Shapiro–Wilk test (SW) and the Anderson–Darling test (AD) turned out to be
strong procedures for testing for normality. They are joined by a class of tests for normality …

We study a novel class of affine invariant and consistent tests for normality in any dimension
in an iid‐setting. The tests are based on a characterization of the standard d‐variate normal …