The rapid emergence of massive datasets in various fields poses a serious challenge to
traditional statistical methods. Meanwhile, it provides opportunities for researchers to …

This paper studies hypothesis testing and parameter estimation in the context of the divide-
and-conquer algorithm. In a unified likelihood based framework, we propose new test …

We propose a novel, efficient approach for distributed sparse learning with observations
randomly partitioned across machines. In each round of the proposed method, worker …

This paper studies distributed estimation and support recovery for high-dimensional linear
regression model with heavy-tailed noise. To deal with heavy-tailed noise whose variance …

In this paper, we develop procedures to construct simultaneous confidence bands for p
potentially infinite-dimensional parameters after model selection for general moment …

ℓ1-penalized quantile regression (ℓ1-QR) is a useful tool for modeling the relationship
between input and output variables when detecting heterogeneous effects in the high …

Three-dimensional (3D) topological Dirac semimetal has a linear energy dispersion in 3D
momentum space, and it can be viewed as an analogue of graphene. Extensive efforts have …

We devise a one-shot approach to distributed sparse regression in the high-dimensional
setting. The key idea is to average" debiased" or" desparsified" lasso estimators. We show …

This paper presents a selective survey of recent developments in statistical inference and
multiple testing for high-dimensional regression models, including linear and logistic …

The alternating direction method of multipliers (ADMM) algorithm is a powerful and flexible
tool for complex optimization problems of the form $\min\{f (x)+ g (y): Ax+ By= c\} $. ADMM …