Characterization and inference of graph diffusion processes from observations of stationary signals
Many tools from the field of graph signal processing exploit knowledge of the underlying
graph's structure (eg, as encoded in the Laplacian matrix) to process signals on the graph …
graph's structure (eg, as encoded in the Laplacian matrix) to process signals on the graph …
A unified framework for structured graph learning via spectral constraints
Graph learning from data is a canonical problem that has received substantial attention in
the literature. Learning a structured graph is essential for interpretability and identification of …
the literature. Learning a structured graph is essential for interpretability and identification of …
An overview of large‐dimensional covariance and precision matrix estimators with applications in chemometrics
The covariance matrix (or its inverse, the precision matrix) is central to many chemometric
techniques. Traditional sample estimators perform poorly for high‐dimensional data such as …
techniques. Traditional sample estimators perform poorly for high‐dimensional data such as …
Gaussian graphical models with applications to omics analyses
Gaussian graphical models (GGMs) provide a framework for modeling conditional
dependencies in multivariate data. In this tutorial, we provide an overview of GGM theory …
dependencies in multivariate data. In this tutorial, we provide an overview of GGM theory …
Causal structure discovery between clusters of nodes induced by latent factors
We consider the problem of learning the structure of a causal directed acyclic graph (DAG)
model in the presence of latent variables. We define" latent factor causal models"(LFCMs) as …
model in the presence of latent variables. We define" latent factor causal models"(LFCMs) as …
Sparse reduced rank Huber regression in high dimensions
We propose a sparse reduced rank Huber regression for analyzing large and complex high-
dimensional data with heavy-tailed random noise. The proposed method is based on a …
dimensional data with heavy-tailed random noise. The proposed method is based on a …
Block-diagonal covariance selection for high-dimensional Gaussian graphical models
E Devijver, M Gallopin - Journal of the American Statistical …, 2018 - Taylor & Francis
Gaussian graphical models are widely used to infer and visualize networks of dependencies
between continuous variables. However, inferring the graph is difficult when the sample size …
between continuous variables. However, inferring the graph is difficult when the sample size …
Skeleton estimation of directed acyclic graphs using partial least squares from correlated data
X Wang, S Lu, R Zhou, H Wang - Pattern Recognition, 2023 - Elsevier
Directed acyclic graphs (DAGs) are directed graphical models that are well known for
discovering causal relationships between variables in a high-dimensional setting. When the …
discovering causal relationships between variables in a high-dimensional setting. When the …
A partial correlation screening approach for controlling the false positive rate in sparse Gaussian graphical models
Abstract Gaussian Graphical Models (GGMs) are extensively used in many research areas,
such as genomics, proteomics, neuroimaging, and psychology, to study the partial …
such as genomics, proteomics, neuroimaging, and psychology, to study the partial …
Learning sparse gaussian graphical models with overlap** blocks
We present a novel framework, called GRAB (GRaphical models with overlAp** Blocks),
to capture densely connected components in a network estimate. GRAB takes as input a …
to capture densely connected components in a network estimate. GRAB takes as input a …