.png){kind=icon}
Return
Joint graphical lasso with regularized aggregation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jmva.2025.105509.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We present methods for estimating multiple precision matrices for high-dimensional time series within the framework of Gaussian graphical models, with a specific focus on analyzing functional magnetic resonance imaging (fMRI) data collected from multiple subjects. Our goal is to estimate both individual brain networks and a collective structure representing a group of subjects. To achieve this, we propose a method that utilizes group Graphical Lasso and regularized aggregation to simultaneously estimate individual and group precision matrices, assigning varying weights to each individual based on their outlier status within the group. We investigate the convergence rates of precision matrix estimators under various norms and expectations, assessing their performance with sub-Gaussian and heavy-tailed data. The effectiveness of our methods is demonstrated through simulations and real fMRI data analysis.
Keywords:
Aggregation
Brain connectivity
Joint estimation
Long-memory
Precision matrix estimation
Regularization
Time series
Journal
J
IF:
1.7
Papers:
99
Citations:
5.8K
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSave
ShareSaveShareSaveShareSave
View more