Proposes a Bayesian spike-and-slab Lasso method combined with imputation-regularization optimization to estimate sparse precision matrices in Gaussian graphical models while correcting for measurement error.
Predicting relapse in favorable histology Wilms tumor using gene expression analysis: a report from the Renal Tumor Commit- tee of the Children’s Oncology Group
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Bayesian Regularization of Gaussian Graphical Models with Measurement Error
Proposes a Bayesian spike-and-slab Lasso method combined with imputation-regularization optimization to estimate sparse precision matrices in Gaussian graphical models while correcting for measurement error.