Inspired by the success of the Lasso for regression analysis, it seems attractive to estimate the graph of a multivariate normal distribution by ℓ1-norm penalized likelihood maximization. We examine some properties of the estimator and show that care has to be taken with interpretation of results as the estimator is not consistent for some graphs. © 2007 Elsevier Ltd. All rights reserved
We study the distributions of the LASSO, SCAD, and thresholding estimators, in finite samples and in...
A Gaussian graphical model is a graph representation of conditional independence relations among Gau...
In many applied fields, such as genomics, different types of data are collected on the same system, ...
We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian grap...
We consider the task of estimating a Gaussian graphical model in the high-dimensional setting. The g...
AbstractWe study the distributions of the LASSO, SCAD, and thresholding estimators, in finite sample...
Building on a recent framework for distributionally robust optimization, we considerestimation of th...
Building on a recent framework for distributionally robust optimization, we considerestimation of th...
Building on a recent framework for distributionally robust optimization, we considerestimation of th...
The graphical lasso [5] is an algorithm for learning the structure in an undirected Gaussian graphic...
Gaussian Graphical Models (GGMs) are widely used for exploratory data analysis in various fields suc...
In more and more applications, a quantity of interest may depend on several covariates, with at leas...
Gaussian graphical models with sparsity in the inverse covariance matrix are of significant interest...
Graphical models are powerful tools when estimating complex dependence structures among large sets o...
Recently, a special case of precision matrix estimation based on a distributionally robust optimizat...
We study the distributions of the LASSO, SCAD, and thresholding estimators, in finite samples and in...
A Gaussian graphical model is a graph representation of conditional independence relations among Gau...
In many applied fields, such as genomics, different types of data are collected on the same system, ...
We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian grap...
We consider the task of estimating a Gaussian graphical model in the high-dimensional setting. The g...
AbstractWe study the distributions of the LASSO, SCAD, and thresholding estimators, in finite sample...
Building on a recent framework for distributionally robust optimization, we considerestimation of th...
Building on a recent framework for distributionally robust optimization, we considerestimation of th...
Building on a recent framework for distributionally robust optimization, we considerestimation of th...
The graphical lasso [5] is an algorithm for learning the structure in an undirected Gaussian graphic...
Gaussian Graphical Models (GGMs) are widely used for exploratory data analysis in various fields suc...
In more and more applications, a quantity of interest may depend on several covariates, with at leas...
Gaussian graphical models with sparsity in the inverse covariance matrix are of significant interest...
Graphical models are powerful tools when estimating complex dependence structures among large sets o...
Recently, a special case of precision matrix estimation based on a distributionally robust optimizat...
We study the distributions of the LASSO, SCAD, and thresholding estimators, in finite samples and in...
A Gaussian graphical model is a graph representation of conditional independence relations among Gau...
In many applied fields, such as genomics, different types of data are collected on the same system, ...
DOI
Add to ORKG