Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates

DOI10.1080/00949655.2017.1398255OpenAlexW2768072440WikidataQ55081482 ScholiaQ55081482MaRDI QIDQ4960562

Wenyaw Chan, Matthew D. Koslovsky, Anna V. Wilkinson, Michael D. Swartz, Luis G. León Novelo

Publication date: 23 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc5935273

zbMATH Keywords

Bayesian inferencevariable selectiondeterministic annealingexpectation-maximizationbinary outcomesinheritance propertygrouped covariatesheredity constraint

Mathematics Subject Classification ID

Related Items (3)

Phase I risk-adjusted Bernoulli chart in multistage healthcare processes based on the state-space model ⋮ The expectation-maximization approach for Bayesian additive Cox regression with current status data ⋮ A novel variational Bayesian method for variable selection in logistic regression models

Uses Software

• R
• hierNet
• gglasso

Cites Work

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• Bayesian variable selection and estimation for group Lasso
• A lasso for hierarchical interactions
• Structured variable selection and estimation
• On the convergence properties of the EM algorithm
• The expectation-maximization approach for Bayesian quantile regression
• Simulation-based regularized logistic regression
• The Group Lasso for Logistic Regression
• The Bayesian Lasso
• Bayesian Model Averaging for Linear Regression Models
• The Variable Selection Problem
• Bayesian Graphical Models for Discrete Data
• Bayesian variable selection with related predictors
• EMVS: The EM Approach to Bayesian Variable Selection
• Variable Selection With the Strong Heredity Constraint and Its Oracle Property
• Variable Selection Using Adaptive Nonlinear Interaction Structures in High Dimensions
• Bayesian Analysis of Binary and Polychotomous Response Data
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
• Model Selection and Estimation in Regression with Grouped Variables
• Bayesian auxiliary variable models for binary and multinomial regression

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