A new approach for ultrahigh dimensional precision matrix estimation
type: Item
instance of: scholarly article
title: A new approach for ultrahigh dimensional precision matrix estimation
author: Wanfeng Liang
author: Yu-Hao Zhang
author: Jiyang Wang
author: Yue Wu
author: Xiaoyan Ma
published in: Journal of Statistical Planning and Inference
publication date: 2024-06-17T00:00:00Z
Mathematics Subject Classification ID: 62-XX
zbMATH DE Number: 7866559
zbMATH Keywords: modified Cholesky decomposition
zbMATH Keywords: precision matrix
zbMATH Keywords: refitted cross validation
zbMATH Keywords: ultrahigh dimension
MaRDI profile type: Publication
cites work: Posterior convergence rates for estimating large precision matrices using graphical models
cites work: Bayesian structure learning in graphical models
cites work: Covariance regularization by thresholding
cites work: A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
cites work: Estimating structured high-dimensional covariance and precision matrices: optimal rates and adaptive estimation
cites work: Posterior graph selection and estimation consistency for high-dimensional Bayesian DAG models
cites work: A permutation-based Bayesian approach for inverse covariance estimation
cites work: Variance Estimation Using Refitted Cross-Validation in Ultrahigh Dimensional Regression
cites work: An overview of the estimation of large covariance and precision matrices
cites work: Sure Independence Screening for Ultrahigh Dimensional Feature Space
cites work: Sparse inverse covariance estimation with the graphical lasso
cites work: Covariance matrix selection and estimation via penalised normal likelihood
cites work: An improved modified cholesky decomposition approach for precision matrix estimation
cites work: Sparsistency and rates of convergence in large covariance matrix estimation
cites work: Estimating Large Precision Matrices via Modified Cholesky Decomposition
cites work: Sparse estimation of large covariance matrices via a nested Lasso penalty
cites work: High-dimensional semiparametric Gaussian copula graphical models
cites work: Fast and adaptive sparse precision matrix estimation in high dimensions
cites work: Minimax estimation of large precision matrices with bandable Cholesky factor
cites work: TIGER: A tuning-insensitive approach for optimally estimating Gaussian graphical models
cites work: Lasso-type recovery of sparse representations for high-dimensional data
cites work: Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
cites work: Best permutation analysis
cites work: Ultrahigh dimensional precision matrix estimation via refitted cross validation
cites work: Cholesky-based model averaging for covariance matrix estimation
DOI: 10.1016/J.JSPI.2024.106164
description: scientific article; zbMATH DE number 7866559