High-dimensional covariance estimation based on Gaussian graphical models

2 September 2010

Papers citing "High-dimensional covariance estimation based on Gaussian graphical models"

29 / 29 papers shown

Title
Discovery and Expansion of New Domains within Diffusion Models Ye Zhu Yu Wu Duo Xu Zhiwei Deng Yan Yan Olga Russakovsky DiffM 20 1 0 13 Oct 2023
Information criteria for structured parameter selection in high dimensional tree and graph models M. Jansen 8 3 0 24 Jun 2023
Sharper rates of convergence for the tensor graphical Lasso estimator Shuheng Zhou Kristjan Greenewald 24 0 0 02 Apr 2023
GraphSPME: Markov Precision Matrix Estimation and Asymptotic Stein-Type Shrinkage Berent AAnund Stromnes Lunde Feda Curic Sondre Sortland 84 1 0 16 May 2022
The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models W. van den Boom A. Beskos Maria de Iorio 14 18 0 03 Aug 2021
High-dimensional Precision Matrix Estimation with a Known Graphical Structure Thi-Tinh-Minh Le Pingshou Zhong 9 6 0 28 Jun 2021
Graphical Elastic Net and Target Matrices: Fast Algorithms and Software for Sparse Precision Matrix Estimation Solt Kovács Tobias Ruckstuhl Helena Obrist Peter Buhlmann 16 9 0 06 Jan 2021
Effective Learning of a GMRF Mixture Model Shahaf E. Finder Eran Treister O. Freifeld 11 5 0 18 May 2020
Gaussian Graphical Model exploration and selection in high dimension low sample size setting Thomas Lartigue Simona Bottani Stéphanie Baron O. Colliot S. Durrleman S. Allassonnière 14 6 0 11 Mar 2020
Learning Gaussian Graphical Models via Multiplicative Weights Anamay Chaturvedi Jonathan Scarlett 11 3 0 20 Feb 2020
The Sylvester Graphical Lasso (SyGlasso) Yu Wang B. Jang Alfred Hero 9 14 0 01 Feb 2020
Gextext: Disease Network Extraction from Biomedical Literature R. O’Shea MedIm 11 1 0 06 Nov 2019
Markov Random Fields for Collaborative Filtering Harald Steck 9 26 0 21 Oct 2019
Graph Learning from Filtered Signals: Graph System and Diffusion Kernel Identification Hilmi E. Egilmez Eduardo Pavez Antonio Ortega 16 66 0 07 Mar 2018
Model-based Clustering with Sparse Covariance Matrices Michael Fop T. B. Murphy Luca Scrucca 16 39 0 21 Nov 2017
Neighborhood selection with application to social networks Nan Wang W. Polonik 17 0 0 16 Nov 2017
Fine-Gray competing risks model with high-dimensional covariates: estimation and Inference Jue Hou Jelena Bradic R. Xu 15 0 0 29 Jul 2017
Graph Learning from Data under Structural and Laplacian Constraints Hilmi E. Egilmez Eduardo Pavez Antonio Ortega 22 42 0 16 Nov 2016
Joint mean and covariance estimation with unreplicated matrix-variate data Michael Hornstein Roger Fan K. Shedden Shuheng Zhou 11 13 0 13 Nov 2016
A review of Gaussian Markov models for conditional independence Irene Córdoba-Sánchez C. Bielza P. Larrañaga VLM 12 0 0 23 Jun 2016
On model misspecification and KL separation for Gaussian graphical models Varun Jog Po-Ling Loh 26 15 0 10 Jan 2015
A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty Ashwini Maurya 19 0 0 26 Dec 2014
Estimation of positive definite M-matrices and structure learning for attractive Gaussian Markov Random fields M. Slawski Matthias Hein 21 104 0 26 Apr 2014
Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models Jean Honorio Tommi Jaakkola 24 25 0 26 Sep 2013
Regularized rank-based estimation of high-dimensional nonparanormal graphical models Lingzhou Xue H. Zou 27 264 0 13 Feb 2013
Gemini: Graph estimation with matrix variate normal instances Shuheng Zhou 50 104 0 23 Sep 2012
Exact covariance thresholding into connected components for large-scale Graphical Lasso Rahul Mazumder Trevor Hastie 48 231 0 18 Aug 2011
Thresholded Lasso for high dimensional variable selection and statistical estimation Shuheng Zhou 102 50 0 08 Feb 2010
Time Varying Undirected Graphs Shuheng Zhou John D. Lafferty Larry A. Wasserman 106 245 0 20 Feb 2008