This article reviews recent progress in high-dimensional bootstrap. We first review high-
dimensional central limit theorems for distributions of sample mean vectors over the …

In this paper, we derive new, nearly optimal bounds for the Gaussian approximation to
scaled averages of n independent high-dimensional centered random vectors X 1,…, X n …

Bridging factor and sparse models Page 1 The Annals of Statistics 2023, Vol. 51, No. 4,
1692–1717 https://doi.org/10.1214/23-AOS2304 © Institute of Mathematical Statistics, 2023 …

We obtain explicit error bounds for the d-dimensional normal approximation on
hyperrectangles for a random vector that has a Stein kernel, or admits an exchangeable pair …

This chapter presents key concepts and theoretical results for analyzing estimation and
inference in high-dimensional models. High-dimensional models are characterized by …

Nonasymptotic bounds for Gaussian and bootstrap approximation have recently attracted
significant interest in high-dimensional statistics. This paper studies Berry–Esseen bounds …

We study the power of query access for the fundamental task of agnostic learning under the
Gaussian distribution. In the agnostic model, no assumptions are made on the labels of the …

In this article, we study the problem of high-dimensional conditional independence testing, a
key building block in statistics and machine learning. We propose an inferential procedure …

For data segmentation in high-dimensional linear regression settings, the regression
parameters are often assumed to be sparse segment-wise, which enables many existing …

We establish general upper bounds on the Kolmogorov distance between two probability
distributions in terms of the distance between these distributions as measured with respect …