16 citations · 50 across the 17 of their papers we have counts for
18 papers
Few-Round Distributed Principal Component Analysis: Closing the Statistical Efficiency Gap by Consensus
ZeYu Li, Xinsheng Zhang, Wang Zhou
Distributed algorithms and theories are called for in this era of big data. Under weaker local signal-to-noise ratios, we improve upon the celebrated one-round distributed principa…
Knowledge Transfer across Multiple Principal Component Analysis Studies
Zeyu Li, Kangxiang Qin, Yong He +2
Transfer learning has aroused great interest in the statistical community. In this article, we focus on knowledge transfer for unsupervised learning tasks in contrast to the superv…
Simultaneous Change Point Detection and Identification for High Dimensional Linear Models
Bin Liu, Xinsheng Zhang, Yufeng Liu
In this article, we consider change point inference for high dimensional linear models. For change point detection, given any subgroup of variables, we propose a new method for tes…
Simultaneous Estimation and Dataset Selection for Transfer Learning in High Dimensions by a Non-convex Penalty
Zeyu Li, Dong Liu, Yong He +1
In this paper, we propose to estimate model parameters and identify informative source datasets simultaneously for high-dimensional transfer learning problems with the aid of a non…
Change Point Detection for High-dimensional Linear Models: A General Tail-adaptive Approach
Bin Liu, Zhengling Qi, Xinsheng Zhang +1
We propose a novel approach for detecting change points in high-dimensional linear regression models. Unlike previous research that relied on strict Gaussian/sub-Gaussian error ass…
Manifold Principle Component Analysis for Large-Dimensional Matrix Elliptical Factor Model
ZeYu Li, Yong He, Xinbing Kong +1
Matrix factor model has been growing popular in scientific fields such as econometrics, which serves as a two-way dimension reduction tool for matrix sequences. In this article, we…
Matrix Factor Analysis: From Least Squares to Iterative Projection
Yong He, Xinbing Kong, Long Yu +2
In this article, we study large-dimensional matrix factor models and estimate the factor loading matrices and factor score matrix by minimizing square loss function. Interestingly,…
Simultaneous Cluster Structure Learning and Estimation of Heterogeneous Graphs for Matrix-variate fMRI Data
Dong Liu, Changwei Zhao, Yong He +3
Graphical models play an important role in neuroscience studies, particularly in brain connectivity analysis. Typically, observations/samples are from several heterogenous groups a…
Simultaneous Differential Network Analysis and Classification for High-dimensional Matrix-variate Data, with application to Brain Connectivity Alteration Detection and fMRI-guided Medical Diagnoses of Alzheimer's Disease
Chen Hao, Guo Ying, He Yong +6
Alzheimer's disease (AD) is the most common form of dementia, which causes problems with memory, thinking and behavior. Growing evidence has shown that the brain connectivity netwo…
Robust Covariance Estimation for High-dimensional Compositional Data with Application to Microbial Communities Analysis
Yong He, Pengfei Liu, Xinsheng Zhang +1
Microbial communities analysis is drawing growing attention due to the rapid development of high-throughput sequencing techniques nowadays. The observed data has the following typi…
Projected Estimation for Large-dimensional Matrix Factor Models
Long Yu, Yong He, Xin-bing Kong +1
In this study, we propose a projection estimation method for large-dimensional matrix factor models with cross-sectionally spiked eigenvalues. By projecting the observation matrix…
Network-Assisted Estimation for Large-dimensional Factor Model with Guaranteed Convergence Rate Improvement
Long Yu, Yong He, Xinsheng Zhang +1
Network structure is growing popular for capturing the intrinsic relationship between large-scale variables. In the paper we propose to improve the estimation accuracy for large-di…
Large-dimensional Factor Analysis without Moment Constraints
Yong He, Xinbing Kong, Long Yu +1
Large-dimensional factor model has drawn much attention in the big-data era, in order to reduce the dimensionality and extract underlying features using a few latent common factors…
High-dimensional Two-sample Precision Matrices Test: An Adaptive Approach through Multiplier Bootstrap
Mingjuan Zhang, Yong He, Cheng Zhou +1
Precision matrix, which is the inverse of covariance matrix, plays an important role in statistics, as it captures the partial correlation between variables. Testing the equality o…
Doubly Robust Sure Screening for Elliptical Copula Regression Model
Yong He, Liang Zhang, Jiadong JI +1
Regression analysis has always been a hot research topic in statistics. We propose a very flexible semi-parametric regression model called Elliptical Copula Regression (ECR) model,…
A Unified Framework for Testing High Dimensional Parameters: A Data-Adaptive Approach
Cheng Zhou, Xinsheng Zhang, Wenxin Zhou +1
High dimensional hypothesis test deals with models in which the number of parameters is significantly larger than the sample size. Existing literature develops a variety of individ…
Robust Factor Number Specification for Large-dimensional Elliptical Factor Model
Long Yu, Yong He, Xinsheng Zhang
The accurate specification of the number of factors is critical to the validity of factor models and the topic almost occupies the central position in factor analysis. Plenty of es…
An Extreme-Value Approach for Testing the Equality of Large U-Statistic Based Correlation Matrices
Cheng Zhou, Fang Han, Xinsheng Zhang +1
There has been an increasing interest in testing the equality of large Pearson's correlation matrices. However, in many applications it is more important to test the equality of la…