Bayesian sparse covariance decomposition with a graphical structure

DOI10.1007/s11222-014-9540-7zbMath1342.62039OpenAlexW2046458055MaRDI QIDQ2631381

Abhra Sarkar, Lin Zhang, Bani. K. Mallick

Publication date: 29 July 2016

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-014-9540-7

zbMATH Keywords

factor analysiscovariance estimationBayesian graphical Lassofactor graphical modellow-rank-plus-sparse decomposition

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Bayesian inference (62F15) Graphical methods in statistics (62A09)

Uses Software

• glasso
• covreg
• spcov

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Sparse estimation of a covariance matrix
• Sparse inverse covariance estimation with the graphical lasso
• Latent variable graphical model selection via convex optimization
• A well-conditioned estimator for large-dimensional covariance matrices
• High dimensional covariance matrix estimation using a factor model
• Bayesian sparse graphical models for classification with application to protein expression data
• Nonparametric Bayesian sparse factor models with application to gene expression modeling
• Factor stochastic volatility with time varying loadings and Markov switching regimes
• Covariance regularization by thresholding
• Bayesian inference for a covariance matrix
• Hyper Markov laws in the statistical analysis of decomposable graphical models
• Regularized estimation of large covariance matrices
• Sparse estimation of large covariance matrices via a nested Lasso penalty
• Posterior contraction in sparse Bayesian factor models for massive covariance matrices
• Bayesian inference for graphical factor analysis models
• Bayesian graphical Lasso models and efficient posterior computation
• Efficient estimation of covariance selection models
• Nonparametric estimation of large covariance matrices of longitudinal data
• A Covariance Regression Model
• Sparse Bayesian infinite factor models
• Robust principal component analysis?
• Rank-Sparsity Incoherence for Matrix Decomposition
• Adaptive Thresholding for Sparse Covariance Matrix Estimation
• The Matrix-Logarithmic Covariance Model
• Model selection and estimation in the Gaussian graphical model
• Miscellanea. Identification of a single-factor model using graphical Gaussian rules
• Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
• Identifiability, Improper Priors, and Gibbs Sampling for Generalized Linear Models
• Efficient Construction of Reversible Jump Markov Chain Monte Carlo Proposal Distributions
• Bayesian Selection on the Number of Factors in a Factor Analysis Model
• Decomposable graphical Gaussian model determination
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions
• Model Averaging and Dimension Selection for the Singular Value Decomposition
• High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics
• On identification of multi-factor models with correlated residuals
• A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models
• Covariance matrix selection and estimation via penalised normal likelihood