Add to ORKGIn the context of Gaussian Graphical Models (GGMs) with high-dimensional small sample data, we present a simple procedure, called PACOSE – standing for PArtial COrrelation SElection – to estimate partial correlations under the constraint that some of them are strictly zero. This method can also be extended to covariance selection. If the goal is to estimate a GGM, our new procedure can be applied to re-estimate the partial correlations after a first graph has been estimated in the hope to improve the estimation of non-zero coefficients. This iterated version of PACOSE is called iPACOSE. In a simulation study, we compare PACOSE to existing methods and show that the re-estimated partial correlation coefficients may be closer to the real value...
We propose a semiparametric approach called the nonparanormal SKEPTIC for efficiently and robustly e...
In recent years several researchers have proposed the use of the Gaussian graphical model de\ufb01ne...
Learning of large--scale networks of interactions from microarray data is an important and challengi...
In the context of Gaussian Graphical Models (GGMs) with high-dimensional small sample data, we prese...
In the context of Gaussian Graphical Models (GGMs) with high- dimensional small sample data, we pre...
The Gaussian graphical model (GGM) is an increasingly popular technique used in psychology to charac...
Gaussian graphical models (GGMs) are network models where randomvariables are represented by nodes a...
We explore various Bayesian approaches to estimate partial Gaussian graphical models. Our hierarchic...
Graphical models have established themselves as fundamental tools through which to understand comple...
Gaussian Graphical Models (GGMs) are extensively used in many research areas, such as genomics, prot...
We explore elliptical graphical models as a generalization of Gaussian graphical models, that is, we...
A graphical model captures conditional relationships among a set of random variables via a graph. Un...
This short paper proves inequalities that restrict the magnitudes of the partial correlations in sta...
We derive a combinatorial sufficient condition for a partial correlation hypersurface in the paramet...
We propose a semiparametric approach called the nonparanormal SKEPTIC for efficiently and robustly e...
In recent years several researchers have proposed the use of the Gaussian graphical model de\ufb01ne...
Learning of large--scale networks of interactions from microarray data is an important and challengi...
In the context of Gaussian Graphical Models (GGMs) with high-dimensional small sample data, we prese...
In the context of Gaussian Graphical Models (GGMs) with high- dimensional small sample data, we pre...
The Gaussian graphical model (GGM) is an increasingly popular technique used in psychology to charac...
Gaussian graphical models (GGMs) are network models where randomvariables are represented by nodes a...
We explore various Bayesian approaches to estimate partial Gaussian graphical models. Our hierarchic...
Graphical models have established themselves as fundamental tools through which to understand comple...
Gaussian Graphical Models (GGMs) are extensively used in many research areas, such as genomics, prot...
We explore elliptical graphical models as a generalization of Gaussian graphical models, that is, we...
A graphical model captures conditional relationships among a set of random variables via a graph. Un...
This short paper proves inequalities that restrict the magnitudes of the partial correlations in sta...
We derive a combinatorial sufficient condition for a partial correlation hypersurface in the paramet...
We propose a semiparametric approach called the nonparanormal SKEPTIC for efficiently and robustly e...
In recent years several researchers have proposed the use of the Gaussian graphical model de\ufb01ne...
Learning of large--scale networks of interactions from microarray data is an important and challengi...