60 citations · 266 across the 46 of their papers we have counts for
46 papers
A Consistent Feature Screening Approach for Tensor Responses with Applications to Genome-Wide Facial Shape Association
Shaofei Zhao, Zuofeng Shang, Seth M. Weinberg +3
As data collecting technologies advance, data structures are getting more and more complex, from single vectors to multi-dimensional tensors. This article is motivated by a variabl…
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…
Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods
Hewei Zhang, Qin Li, Yanxing Yang +4
Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections (CMEs). They are the most important sources of space weather effects, that can…
Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary
Tianyang Hu, Ruiqi Liu, Zuofeng Shang +1
Deep learning has gained huge empirical successes in large-scale classification problems. In contrast, there is a lack of statistical understanding about deep learning methods, par…
Deep Neural Network Classifier for Multi-dimensional Functional Data
Shuoyang Wang, Guanqun Cao, Zuofeng Shang
We propose a new approach, called as functional deep neural network (FDNN), for classifying multi-dimensional functional data. Specifically, a deep neural network is trained based…
Information-theoretic Limits for Testing Community Structures in Weighted Networks
Mingao Yuan, Zuofeng Shang
Community detection refers to the problem of clustering the nodes of a network into groups. Existing inferential methods for community structure mainly focus on unweighted (binary)…
Statistical Inference for Functional Linear Quantile Regression
Peijun Sang, Zuofeng Shang, Pang Du
We propose inferential tools for functional linear quantile regression where the conditional quantile of a scalar response is assumed to be a linear functional of a functional cova…
Statistical Limits for Testing Correlation of Hypergraphs
Mingao Yuan, Zuofeng Shang
In this paper, we consider the hypothesis testing of correlation between two -uniform hypergraphs on unlabelled nodes. Under the null hypothesis, the hypergraphs are indepen…
An Approach of Bayesian Variable Selection for Ultrahigh Dimensional Multivariate Regression
Xiaotian Dai, Guifang Fu, Randall Reese +2
In many practices, scientists are particularly interested in detecting which of the predictors are truly associated with a multivariate response. It is more accurate to model multi…
Calibrating multi-dimensional complex ODE from noisy data via deep neural networks
Kexuan Li, Fangfang Wang, Ruiqi Liu +2
Ordinary differential equations (ODEs) are widely used to model complex dynamics that arises in biology, chemistry, engineering, finance, physics, etc. Calibration of a complicated…
Information Limits for Detecting a Subhypergraph
Mingao Yuan, Zuofeng Shang
We consider the problem of recovering a subhypergraph based on an observed adjacency tensor corresponding to a uniform hypergraph. The uniform hypergraph is assumed to contain a su…
Online Statistical Inference for Parameters Estimation with Linear-Equality Constraints
Ruiqi Liu, Mingao Yuan, Zuofeng Shang
Stochastic gradient descent (SGD) and projected stochastic gradient descent (PSGD) are scalable algorithms to compute model parameters in unconstrained and constrained optimization…
Distributed Adaptive Nearest Neighbor Classifier: Algorithm and Theory
Ruiqi Liu, Ganggang Xu, Zuofeng Shang
When data is of an extraordinarily large size or physically stored in different locations, the distributed nearest neighbor (NN) classifier is an attractive tool for classification…
Heterogeneous Dense Subhypergraph Detection
Mingao Yuan, Zuofeng Shang
We study the problem of testing the existence of a heterogeneous dense subhypergraph. The null hypothesis corresponds to a heterogeneous Erdös-Rényi uniform random hypergraph and t…
Sharp detection boundaries on testing dense subhypergraph
Mingao Yuan, Zuofeng Shang
We study the problem of testing the existence of a dense subhypergraph. The null hypothesis is an Erdos-Renyi uniform random hypergraph and the alternative hypothesis is a uniform…
Estimation of the Mean Function of Functional Data via Deep Neural Networks
Shuoyang Wang, Guanqun Cao, Zuofeng Shang
In this work, we propose a deep neural network method to perform nonparametric regression for functional data. The proposed estimators are based on sparsely connected deep neural n…
A Computationally Efficient Classification Algorithm in Posterior Drift Model: Phase Transition and Minimax Adaptivity
Ruiqi Liu, Kexuan Li, Zuofeng Shang
In massive data analysis, training and testing data often come from very different sources, and their probability distributions are not necessarily identical. A feature example is…
On Deep Instrumental Variables Estimate
Ruiqi Liu, Zuofeng Shang, Guang Cheng
The endogeneity issue is fundamentally important as many empirical applications may suffer from the omission of explanatory variables, measurement error, or simultaneous causality.…
Sharp Rate of Convergence for Deep Neural Network Classifiers under the Teacher-Student Setting
Tianyang Hu, Zuofeng Shang, Guang Cheng
Classifiers built with neural networks handle large-scale high dimensional data, such as facial images from computer vision, extremely well while traditional statistical methods of…