16 citations · 53 across the 24 of their papers we have counts for
6 papers · 1 filter
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.…