Yurinskii's coupling is a popular tool for finite-sample distributional approximation in
mathematical statistics and applied probability, offering a Gaussian strong approximation for …

This paper presents uniform estimation and inference theory for a large class of
nonparametric partitioning-based M-estimators. The main theoretical results include:(i) …

This paper presents novel methods and theories for estimation and inference about
parameters in econometric models using machine learning for nuisance parameters …

We study estimation, pointwise and simultaneous inference, and confidence intervals for
many average partial effects of lasso Logit. Focusing on high-dimensional, cluster-sampling …

We apply Stein's method to investigate the normal approximation for both non-degenerate
and degenerate U-statistics with cross-sectionally dependent underlying processes in the …

This article studies the asymptotic properties of and alternative inference methods for kernel
density estimation (KDE) for dyadic data. We first establish uniform convergence rates for …

Nonparametric methods are central to modern statistics, enabling data analysis with minimal
assumptions in a wide range of scenarios. While contemporary procedures such as random …

In today's big data world, we have witnessed rapidly increasing popularity of machine
learning methods in empirical studies, such as random forests, lasso, post-lasso, elastic …

This supplement is self-contained. It presents more general theoretical results than those
reported in the paper, as well as their proofs. In particular, a larger class of loss functions is …

This supplement collects all technical proofs, more general theoretical results than those
reported in the main paper, and other methodological results. New theoretical results for …