16 citations · 53 across the 24 of their papers we have counts for
24 papers
Robust Statistical Inference for Large-dimensional Matrix-valued Time Series via Iterative Huber Regression
Yong He, Xin-Bing Kong, Dong Liu +1
Matrix factor model is drawing growing attention for simultaneous two-way dimension reduction of well-structured matrix-valued observations. This paper focuses on robust statistica…
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…
Huber Principal Component Analysis for Large-dimensional Factor Models
Yong He, Lingxiao Li, Dong Liu +1
Factor models have been widely used in economics and finance. However, the heavy-tailed nature of macroeconomic and financial data is often neglected in the existing literature. To…
Robust Tensor Factor Analysis
Matteo Barigozzi, Yong He, Lingxiao Li +1
We consider (robust) inference in the context of a factor model for tensor-valued sequences. We study the consistency of the estimated common factors and loadings space when using…
An Efficient Iterative Least Squares Algorithm for Large-dimensional Matrix Factor Model via Random Projection
Yong He, Ran Zhao, Wen-Xin Zhou
The matrix factor model has drawn growing attention for its advantage in achieving two-directional dimension reduction simultaneously for matrix-structured observations. In this pa…
A new non-parametric Kendall's tau for matrix-valued elliptical observations
Yong He, Yalin Wang, Long Yu +2
In this article, we first propose generalized row/column matrix Kendall's tau for matrix-variate observations that are ubiquitous in areas such as finance and medical imaging. For…
Statistical inference for large-dimensional tensor factor model by iterative projections
Matteo Barigozzi, Yong He, Lingxiao Li +1
Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order large-dimensional tensor time series, and have wide applications in economics, finance and medical…
Distributed Learning for Principle Eigenspaces without Moment Constraints
Yong He, Zichen Liu, Yalin Wang
Distributed Principal Component Analysis (PCA) has been studied to deal with the case when data are stored across multiple machines and communication cost or privacy concerns prohi…
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…
Online Change-point Detection for Matrix-valued Time Series with Latent Two-way Factor Structure
Yong He, Xin-bing Kong, Lorenzo Trapani +1
This paper proposes a novel methodology for the online detection of changepoints in the factor structure of large matrix time series. Our approach is based on the well-known fact t…
Transfer Learning in High-dimensional Semi-parametric Graphical Models with Application to Brain Connectivity Analysis
Yong He, Qiushi Li, Qinqin Hu +1
Transfer learning has drawn growing attention with the target of improving statistical efficiency of one study (dataset) by digging information from similar and related auxiliary s…
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…
One-way or Two-way Factor Model for Matrix Sequences?
Yong He, Xin-bing Kong, Lorenzo Trapani +1
This paper investigates the issue of determining the dimensions of row and column factor spaces in matrix-valued data. Exploiting the eigen-gap in the spectrum of sample second mom…
Joint Learning of Multiple Differential Networks with fMRI data for Brain Connectivity Alteration Detection
Hao Chen, Ying Guo, Yong He +3
In this study we focus on the problem of joint learning of multiple differential networks with function Magnetic Resonance Imaging (fMRI) data sets from multiple research centers.…
Quantile factor analysis for large-dimensional time series with statistical guarantee
He Yong, Kong Xin-Bing, Yu Long +1
Quantile is an important measure in finance and quality assessment in service industry. In this paper, we model the temporal and cross-sectional interactive effect of the quantiles…
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,…
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…