SpiderLearner: an ensemble approach to Gaussian graphical model estimation

DOI10.1002/SIM.9714zbMATH Open1545.62561MaRDI QIDQ6625751

Katherine H. Shutta, Denise M. Scholtens, Laura Balzer, Raji Balasubramanian

Publication date: 28 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

networksovarian cancerGaussian graphical modelsgene expressionsuper learnerensemble models

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Title not available (Why is that?)
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• Sparse inverse covariance estimation with the graphical lasso
• Stacked regressions
• Bootstrap inference for network construction with an application to a breast cancer microarray study
• A note on the lack of symmetry in the graphical lasso
• Joint estimation of multiple high-dimensional precision matrices
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• Authoritative sources in a hyperlinked environment
• Oracle inequalities for multi-fold cross validation
• Model selection and estimation in the Gaussian graphical model
• Stability Selection
• An Introduction to Statistical Learning
• The huge Package for High-dimensional Undirected Graph Estimation in R
• Super Learner
• On the Continuity of the Generalized Inverse
• Gaussian graphical models with applications to omics analyses

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