60 citations · 266 across the 46 of their papers we have counts for
6 papers · 1 filter
Scalable Statistical Inference in Non-parametric Least Squares
Meimei Liu, Zuofeng Shang, Yun Yang
Stochastic approximation (SA) is a powerful and scalable computational method for iteratively estimating the solution of optimization problems in the presence of randomness, partic…
Empirical likelihood test for community structure in networks
Mingao Yuan, Sharmin Hossain, Zuofeng Shang
Network data, characterized by interconnected nodes and edges, is pervasive in various domains and has gained significant popularity in recent years. In network data analysis, test…
Matrix Autoregressive Model with Vector Time Series Covariates for Spatio-Temporal Data
Hu Sun, Zuofeng Shang, Yang Chen
We develop a new methodology for forecasting matrix-valued time series with historical matrix data and auxiliary vector time series data. We focus on a time series of matrices defi…
Minimax optimal high-dimensional classification using deep neural networks
Shuoyang Wang, Zuofeng Shang
High-dimensional classification is a fundamentally important research problem in high-dimensional data analysis. In this paper, we derive a nonasymptotic rate for the minimax exces…
Statistical Inference with Stochastic Gradient Methods under -mixing Data
Ruiqi Liu, Xi Chen, Zuofeng Shang
Stochastic gradient descent (SGD) is a scalable and memory-efficient optimization algorithm for large datasets and stream data, which has drawn a great deal of attention and popula…
Scalable inference in functional linear regression with streaming data
Jinhan Xie, Enze Shi, Peijun Sang +3
Traditional static functional data analysis is facing new challenges due to streaming data, where data constantly flow in. A major challenge is that storing such an ever-increasing…