Combining a relaxed EM algorithm with Occam's razor for Bayesian variable selection in high-dimensional regression

DOI10.1016/j.jmva.2015.09.004zbMath1334.62114OpenAlexW1803361327MaRDI QIDQ268752

Julien Chiquet, Pierre Latouche, Pierre-Alexandre Mattei, Charles Bouveyron

Publication date: 15 April 2016

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2015.09.004

zbMATH Keywords

EM algorithmhigh-dimensional datalinear regressionvariable selectionOccam's razorspike-and-slab

Mathematics Subject Classification ID

Related Items (3)

An introduction to recent advances in high/infinite dimensional statistics ⋮ A novel variational Bayesian method for variable selection in logistic regression models ⋮ Bayesian variable selection for globally sparse probabilistic PCA

Uses Software

• R
• CRAN
• fda (R)
• LBFGS-B
• PDCO
• spikeslab
• spinyReg
• Bolasso
• EMVS

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