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 …

Nonparametric regression methods provide an alternative approach to parametric
estimation that requires only weak identification assumptions and thus minimizes the risk of …

This paper studies the nonparametric modal regression problem systematically from a
statistical learning viewpoint. Originally motivated by pursuing a theoretical understanding of …

We study the plug-in estimator for density level sets under Hausdorff loss. We derive
asymptotic theory for this estimator, and based on this theory, we develop two bootstrap …

Modal regression is a good alternative of the mean regression and likelihood based
methods, because of its robustness and high efficiency. A robust communication-efficient …

In this paper, we consider estimation of the conditional mode of an outcome variable given
regressors. To this end, we propose and analyze a computationally scalable estimator …

Asymptotic theory for density ridges Page 1 The Annals of Statistics 2015, Vol. 43, No. 5,
1896–1928 DOI: 10.1214/15-AOS1329 © Institute of Mathematical Statistics, 2015 …

Recently, a number of statistical problems have found an unexpected solution by inspecting
them through a 'modal point of view'. These include classical tasks such as clustering or …

We review recent advances in modal regression studies using kernel density estimation.
Modal regression is an alternative approach for investigating the relationship between a …

Sparse additive models have been successfully applied to high-dimensional data analysis
due to the flexibility and interpretability of their representation. However, the existing …