We establish a necessary and sufficient condition for the existence of the precision matrix estimator obtained by minimizing the negative Gaussian log-likelihood plus a weighted bridge penalty. This condition enables us to connect the literature on Gaussian graphical models to the literature on penalized Gaussian likelihood.Fil: Rothman, Adam J.. University of Minnesota. Scool Of Statistics; Estados UnidosFil: Forzani, Liliana Maria. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Matemática Aplicada "Litoral"; Argentin
In this paper, we estimate the high dimensional precision matrix under the weak sparsity condition w...
High-dimensional data are often most plausibly generated from distributions with complex structure a...
In this article, we focus on the estimation of a high-dimensional inverse covariance (i.e., precisio...
This paper focuses on exploring the sparsity of the inverse covariance matrix $\bSigma^{-1}$, or the...
The majority of methods for sparse precision matrix estimation rely on computationally expensive pro...
We consider the statistical inference for high-dimensional precision matrices. Specifically, we prop...
Under the Gaussian assumption, the relationship between conditional independence and sparsity allows...
We consider the problem of estimating a sparse dynamic Gaussian graphical model with L1 penalized ma...
Abstract—Estimating large sparse precision matrices is an in-teresting and challenging problem in ma...
The accurate estimation of a precision matrix plays a crucial role in the current age of high-dimens...
The thesis considers the estimation of sparse precision matrices in the highdimensional setting. Fir...
Standard likelihood penalties to learn Gaussian graphical models are based on regularising the off-d...
<p>It is known that the accuracy of the maximum likelihood-based covariance and precision matrix est...
Abstract It is known that the accuracy of the maximum likelihood-based covariance and precision matr...
The estimation of a precision matrix has an important role in several research fields. In high-dimen...
In this paper, we estimate the high dimensional precision matrix under the weak sparsity condition w...
High-dimensional data are often most plausibly generated from distributions with complex structure a...
In this article, we focus on the estimation of a high-dimensional inverse covariance (i.e., precisio...
This paper focuses on exploring the sparsity of the inverse covariance matrix $\bSigma^{-1}$, or the...
The majority of methods for sparse precision matrix estimation rely on computationally expensive pro...
We consider the statistical inference for high-dimensional precision matrices. Specifically, we prop...
Under the Gaussian assumption, the relationship between conditional independence and sparsity allows...
We consider the problem of estimating a sparse dynamic Gaussian graphical model with L1 penalized ma...
Abstract—Estimating large sparse precision matrices is an in-teresting and challenging problem in ma...
The accurate estimation of a precision matrix plays a crucial role in the current age of high-dimens...
The thesis considers the estimation of sparse precision matrices in the highdimensional setting. Fir...
Standard likelihood penalties to learn Gaussian graphical models are based on regularising the off-d...
<p>It is known that the accuracy of the maximum likelihood-based covariance and precision matrix est...
Abstract It is known that the accuracy of the maximum likelihood-based covariance and precision matr...
The estimation of a precision matrix has an important role in several research fields. In high-dimen...
In this paper, we estimate the high dimensional precision matrix under the weak sparsity condition w...
High-dimensional data are often most plausibly generated from distributions with complex structure a...
In this article, we focus on the estimation of a high-dimensional inverse covariance (i.e., precisio...