Block-Diagonal Covariance Selection for High-Dimensional Gaussian Graphical Models

DOI10.1080/01621459.2016.1247002zbMath1398.62020arXiv1511.04033OpenAlexW2963129728MaRDI QIDQ4690959

Emilie Devijver, Mélina Gallopin

Publication date: 23 October 2018

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1511.04033

zbMATH Keywords

variable selectionnetwork inferencegraphical lassoadaptive minimax theorynonasymptotic model selection

Mathematics Subject Classification ID

Multivariate analysis (62H99) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Minimax procedures in statistical decision theory (62C20) Graphical methods in statistics (62A09)

Related Items (9)

Block-Diagonal Covariance Estimation and Application to the Shapley Effects in Sensitivity Analysis ⋮ Joint rank and variable selection for parsimonious estimation in a high-dimensional finite mixture regression model ⋮ Structured learning of time-varying networks with application to PM_2.5 data ⋮ Incorporating grouping information into Bayesian Gaussian graphical model selection ⋮ Block-diagonal test for high-dimensional covariance matrices ⋮ Approximation with a Kronecker product structure with one component as compound symmetry or autoregression via entropy loss function ⋮ Robust Bayesian model selection for variable clustering with the Gaussian graphical model ⋮ Testing for independence of large dimensional vectors ⋮ A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models

Uses Software

• igraph
• glasso
• huge
• GGMselect
• QUIC
• CAPUSHE
• recount

Cites Work

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