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 …

We discuss inference after data exploration, with a particular focus on inference after model
or variable selection. We review three popular approaches to this problem: sample splitting …

Combining individual p-values to aggregate multiple small effects has a long-standing
interest in statistics, dating back to the classic Fisher's combination test. In modern large …

Concentration inequalities form an essential toolkit in the study of high-dimensional
statistical methods. Most of the relevant statistics literature in this regard is, however, based …

Improved central limit theorem and bootstrap approximations in high dimensions Page 1 The
Annals of Statistics 2022, Vol. 50, No. 5, 2562–2586 https://doi.org/10.1214/22-AOS2193 © …

We propose a residual and wild bootstrap methodology for individual and simultaneous
inference in high-dimensional linear models with possibly non-Gaussian and …

Distribution grids constitute complex networks of lines oftentimes reconfigured to minimize
losses, balance loads, alleviate faults, or for maintenance purposes. Topology monitoring …

We propose a quantile‐based measure of conditional skewness, particularly suitable for
handling recalcitrant emerging market (EM) returns. The skewness of international stock …

Random forests are among the most popular off-the-shelf supervised learning algorithms.
Despite their well-documented empirical success, however, until recently, few theoretical …

Uncertainty quantification is a fundamental problem in the analysis and interpretation of
synthetic control (SC) methods. We develop conditional prediction intervals in the SC …