We review recent developments in the econometrics of shape restrictions and their role in
applied work. Our objectives are threefold. First, we aim to emphasize the diversity of …

We discuss a number of resampling schemes in which m= o (n) observations are resampled.
We review nonparametric bootstrap failure and give results old and new on how the m out of …

This article studies an asymptotic framework for conducting inference on parameters of the
form, where is a known directionally differentiable function and is estimated by. In these …

RESEARCHERS IN ECONOMETRICS sometimes view the bootstrap as a panacea for
statistical inference. Indeed, it does have widespread applicability; eg, see Hall (1992), Efron …

The Wasserstein distance is an attractive tool for data analysis but statistical inference is
hindered by the lack of distributional limits. To overcome this obstacle, for probability …

Optimal transport (OT) is a versatile framework for comparing probability measures, with
many applications to statistics, machine learning and applied mathematics. However, OT …

A dynamic treatment regime consists of a set of decision rules that dictate how to
individualize treatment to patients based on available treatment and covariate history. A …

We examine challenges to estimation and inference when the objects of interest are
nondifferentiable functionals of the underlying data distribution. This situation arises in a …

This paper considers inference based on a test statistic that has a limit distribution that is
discontinuous in a parameter. The paper shows that subsampling and m out of n bootstrap …

A unifying approach to distributional limits for empirical optimal transport Page 1 Bernoulli
30(4), 2024, 2846–2877 https://doi.org/10.3150/23-BEJ1697 A unifying approach to …