Estimation of positive definite \(M\)-matrices and structure learning for attractive Gaussian Markov random fields
From MaRDI portal
Publication:2341885
DOI10.1016/j.laa.2014.04.020zbMath1312.62070arXiv1404.6640OpenAlexW2963089591MaRDI QIDQ2341885
Publication date: 6 May 2015
Published in: Linear Algebra and its Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1404.6640
\(M\)-matrices\(\ell_1\)-regularizationprecision matrix estimationgraphical model selectionpartial correlationsGaussian Markov random fieldssign constraintshigh-dimensional statistical inferencelog-determinant divergence minimization
Estimation in multivariate analysis (62H12) Parametric inference under constraints (62F30) Convex programming (90C25) Sign pattern matrices (15B35)
Related Items
Total positivity in exponential families with application to binary variables ⋮ Multiway \(p\)-spectral graph cuts on Grassmann manifolds ⋮ A Unified Framework for Structured Graph Learning via Spectral Constraints ⋮ Unnamed Item ⋮ Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications ⋮ A multitest procedure for testing MTP2 for Gaussian distributions ⋮ Optimal rates for estimation of two-dimensional totally positive distributions ⋮ Convolutions of totally positive distributions with applications to kernel density estimation ⋮ Total positivity in multivariate extremes ⋮ Dependence in elliptical partial correlation graphs ⋮ Maximum likelihood estimation in Gaussian models under total positivity ⋮ Bimonotone subdivisions of point configurations in the plane ⋮ Estimation of positive definite \(M\)-matrices and structure learning for attractive Gaussian Markov random fields ⋮ Locally associated graphical models and mixed convex exponential families
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Sparse inverse covariance estimation with the graphical lasso
- Geometry of maximum likelihood estimation in Gaussian graphical models
- Statistics for high-dimensional data. Methods, theory and applications.
- M-matrices as covariance matrices of multinormal distributions
- Classes of orderings of measures and related correlation inequalities. I. Multivariate totally positive distributions
- Causation, prediction, and search
- Sparse permutation invariant covariance estimation
- High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
- Sign-constrained least squares estimation for high-dimensional regression
- Network exploration via the adaptive LASSO and SCAD penalties
- Estimation of positive definite \(M\)-matrices and structure learning for attractive Gaussian Markov random fields
- Total positivity order and the normal distribution
- High-dimensional graphs and variable selection with the Lasso
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- Model selection and estimation in the Gaussian graphical model
- Estimation of a covariance matrix with zeros
- Matrix Nearness Problems with Bregman Divergences
- Asymptotic Statistics
- A Comparison of Block Pivoting and Interior-Point Algorithms for Linear Least Squares Problems with Nonnegative Variables
- Likelihood-Based Selection and Sharp Parameter Estimation
- High-dimensional covariance estimation based on Gaussian graphical models
- High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion
- Approximating discrete probability distributions with dependence trees