Wishart distributions for decomposable covariance graph models
From MaRDI portal
Publication:2429939
DOI10.1214/10-AOS841zbMath1274.62369arXiv1103.1768MaRDI QIDQ2429939
Bala Rajaratnam, Kshitij Khare
Publication date: 5 April 2011
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1103.1768
Multivariate distribution of statistics (62H10) Estimation in multivariate analysis (62H12) Bayesian inference (62F15) Bayesian problems; characterization of Bayes procedures (62C10)
Related Items (25)
Scaling it up: stochastic search structure learning in graphical models ⋮ Stable estimation of a covariance matrix guided by nuclear norm penalties ⋮ Estimation of conditional mean operator under the bandable covariance structure ⋮ The role of the isotonizing algorithm in Stein's covariance matrix estimator ⋮ A generalized likelihood ratio test for normal mean when \(p\) is greater than \(n\) ⋮ Perturbations and projections of Kalman-Bucy semigroups ⋮ Linear optimization over homogeneous matrix cones ⋮ Covariance structure estimation with Laplace approximation ⋮ Estimating high dimensional covariance matrices: a new look at the Gaussian conjugate framework ⋮ Comment on: Sequences of regressions and their independences ⋮ A DC Programming Approach for Sparse Estimation of a Covariance Matrix ⋮ Retaining positive definiteness in thresholded matrices ⋮ Wishart laws and variance function on homogeneous cones ⋮ Wishart distributions for decomposable covariance graph models ⋮ Sparse matrix decompositions and graph characterizations ⋮ Modeling correlated marker effects in genome-wide prediction via Gaussian concentration graph models ⋮ A skew Gaussian decomposable graphical model ⋮ On generating random Gaussian graphical models ⋮ Conjugate and conditional conjugate Bayesian analysis of discrete graphical models of marginal independence ⋮ On the Letac-Massam conjecture and existence of high dimensional Bayes estimators for graphical models ⋮ Nonlinear shrinkage estimation of large-dimensional covariance matrices ⋮ A Gibbs sampler for learning DAG: a unification for discrete and Gaussian domains ⋮ Gaussian covariance faithful Markov trees ⋮ The beta-mixture shrinkage prior for sparse covariances with near-minimax posterior convergence rate ⋮ Linear Inverse Problem with Range Prior on Correlations and Its Variational Bayes Inference
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Positive definite completions of partial Hermitian matrices
- Covariance chains
- Flexible covariance estimation in graphical Gaussian models
- Schur products and matrix completions
- Conjugate priors for exponential families
- Hyper Markov laws in the statistical analysis of decomposable graphical models
- Enriched conjugate and reference priors for the Wishart family on symmetric cones
- Linear dependencies represented by chain graphs. With comments and a rejoinder by the authors
- Wishart distributions on homogeneous cones
- On the convergence of the Markov chain simulation method
- Wishart distributions for decomposable covariance graph models
- Wishart distributions for decomposable graphs
- Graphical methods for efficient likelihood inference in Gaussian covariance models
- Estimation of a covariance matrix with zeros
- Cholesky Decompositions and Estimation of A Covariance Matrix: Orthogonality of Variance Correlation Parameters
- Linear Recursive Equations, Covariance Selection, and Path Analysis
- Bayesian analysis of covariance matrices and dynamic models for longitudinal data
- Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
- Introduction to Graphical Modelling
- Cholesky decomposition of a hyper inverse Wishart matrix
- On identification of multi-factor models with correlated residuals
- Covariance matrix selection and estimation via penalised normal likelihood
This page was built for publication: Wishart distributions for decomposable covariance graph models