.png){kind=icon}
Return
Multiple matrix Gaussian graphs estimation
.png){kind=icon}
OA
AI
DOI：10.1111/rssb.12278.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Matrix-valued data, where the sampling unit is a matrix consisting of rows and columns of measurements, are emerging in numerous scientific and business applications. Matrix Gaussian graphical models are a useful tool to characterize the conditional dependence structure of rows and columns. We employ non-convex penalization to tackle the estimation of multiple graphs from matrix-valued data under a matrix normal distribution. We propose a highly efficient non-convex optimization algorithm that can scale up for graphs with hundreds of nodes. We establish the asymptotic properties of the estimator, which requires less stringent conditions and has a sharper probability error bound than existing results. We demonstrate the efficacy of our proposed method through both simulations and real functional magnetic resonance imaging analyses.
Keywords:
Conditional independence
Gaussian graphical model
Matrix normal distribution
Non-convex penalization
Resting state functional magnetic resonance imaging
Sparsistency
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3.6
Papers:
1.5K
Citations:
3.2W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
View more