Bayesian variable selection for logistic regression

DOI10.1002/sam.11428OpenAlexW2955592940MaRDI QIDQ4970277

Howard D. Bondell, Alyson G. Wilson, Yiqing Tian

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11343/286050

zbMATH Keywords

Lassonormal-gamma priorLaplace priorjoint credible region

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Uses Software

• glmnet

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• On Bayesian model and variable selection using MCMC
• Informative \(g\)-priors for logistic regression
• Estimating the dimension of a model
• Bayesian estimation of sparse signals with a continuous spike-and-slab prior
• Least angle regression. (With discussion)
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Inference with normal-gamma prior distributions in regression problems
• Variable Selection in Logistic Regression Models
• The Bayesian Lasso
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• The Spike-and-Slab LASSO
• Bayes Factors
• Consistent High-Dimensional Bayesian Variable Selection via Penalized Credible Regions
• Bayesian Regularization for Graphical Models With Unequal Shrinkage
• Regularization and Variable Selection Via the Elastic Net
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
• Model Selection and Estimation in Regression with Grouped Variables
• An invariant form for the prior probability in estimation problems
• A new look at the statistical model identification

This page was built for publication: Bayesian variable selection for logistic regression