activity
20242026
collaborators

7 papers

stat.ME2026

Personalized Federated Vector Autoregression with Personalization Diversity

Zhiyun Fan, Xiaoyu Zhang, Guodong Li +1

Personalized federated learning can improve estimation for high-dimensional time series while preserving client-specific dynamics. However, parameter heterogeneity creates a fundam…

stat.ME2026

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…

stat.ME2026

Reduced-Rank Network Autoregression with Grouped Edge Effects

Qi Lyu, Xiaoyu Zhang, Guodong Li +1

We propose the Edge-Grouped Reduced-Rank Network Autoregressive model (Edge-Grouped RRNAR) for multivariate time series observed over a network. The model allows transmission effec…

stat.ME2025

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…

stat.ME2025

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…

stat.ME2025

A Hybrid Framework Combining Autoregression and Common Factors for Matrix Time Series

Zhiyun Fan, Xiaoyu Zhang, Di Wang

Matrix-valued time series are ubiquitous in modern economics and finance, yet modeling them requires navigating a trade-off between flexibility and parsimony. We propose the Matrix…

stat.ME2024

Robust Gradient Descent Estimation for Tensor Models under Heavy-Tailed Distributions

Xiaoyu Zhang, Di Wang, Guodong Li +1

Low-rank tensor models are widely used in statistics. However, most existing methods rely heavily on the assumption that data follows a sub-Gaussian distribution. To address the ch…