Wasserstein distances are metrics on probability distributions inspired by the problem of
optimal mass transportation. Roughly speaking, they measure the minimal effort required to …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Nonparametric two-sample or homogeneity testing is a decision theoretic problem that
involves identifying differences between two random variables without making parametric …

Precipitation's temporal and spatial patterns under climate change significantly impact
global terrestrial ecology and human social activities. Climate models are essential tools for …

This book is mainly about goodness-of-fit testing, particularly about tests for the one-and the
two-and K-sample problems. In the one-sample problem we need to test the hypothesis that …

Goodness-of-fit tests based on the empirical Wasserstein distance are proposed for simple
and composite null hypotheses involving general multivariate distributions. For group …

We study the asymptotic properties of Lasso+ mLS and Lasso+ Ridge under the sparse high-
dimensional linear regression model: Lasso selecting predictors and then modified Least …

We derive distributional limits for empirical transport distances between probability
measures supported on countable sets. Our approach is based on sensitivity analysis of …

We derive central limit theorems for the Wasserstein distance between the empirical
distributions of Gaussian samples. The cases are distinguished whether the underlying laws …

We search for methods or tools to detect whether the 1-dimensional marginal distribution of
traffic increments of aggregate TCP-traffic satisfy the hypothesis of approximate normality …