5 papers
Two-Stage Robust Sparse Gradient Methods for Regression Under Heavy-Tailed Designs
Kaiyuan Zhou, Xiaoyu Zhang, Wenyang Zhang +1
We study high-dimensional sparse regression under simultaneous heavy-tailed covariates and noise. Heavy-tailed data affect sparse optimization in two different ways: extreme covari…
Complex trend inference for high-dimensional piecewise locally stationary time series
Lujia Bai, David Veitch, Weichi Wu +2
This paper studies high-dimensional trend inference for piecewise smooth signals under nonstationary noise and asynchronous structural breaks by first detecting asynchronous change…
Scale-Invariant Robust Estimation of High-Dimensional Kronecker-Structured Matrices
Xiaoyu Zhang, Zhiyun Fan, Wenyang Zhang +1
High-dimensional Kronecker-structured estimation faces a conflict between non-convex scaling ambiguities and statistical robustness. The arbitrary factor scaling distorts gradient…
High-dimensional low-rank matrix regression with unknown latent structures
Di Wang, Xiaoyu Zhang, Guodong Li +1
We study low-rank matrix regression in settings where matrix-valued predictors and scalar responses are observed across multiple individuals. Rather than assuming a fully homogeneo…
Robust estimation for high-dimensional time series with heavy tails
Yu Wang, Guodong Li, Zhijie Xiao +2
We study in this paper the problem of least absolute deviation (LAD) regression for high-dimensional heavy-tailed time series which have finite -th moment with . T…