Incorporating spatial structure into inclusion probabilities for Bayesian variable selection in generalized linear models with the spike-and-slab elastic net

DOI10.1016/j.jspi.2021.07.010zbMath1478.62208OpenAlexW3196749099MaRDI QIDQ2059435

Nengjun Yi, Justin M. Leach, Inmaculada B. Aban

Publication date: 14 December 2021

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2021.07.010

zbMATH Keywords

generalized linear modelsAlzheimer's diseasepenalized likelihoodspike-and-slabBayesian variable selectionelastic net

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Generalized linear models (logistic models) (62J12)

Cites Work

• Pathwise coordinate optimization
• The Bayesian elastic net
• Generalized Hierarchical Multivariate CAR Models for Areal Data
• Incorporating Predictor Network in Penalized Regression with Application to Microarray Data
• The Bayesian Lasso
• Bayesian Variable Selection in Linear Regression
• Gaussian Markov Random Fields
• The Spike-and-Slab LASSO
• EMVS: The EM Approach to Bayesian Variable Selection
• Bayesian Variable Selection in Structured High-Dimensional Covariate Spaces With Applications in Genomics
• Spatial Bayesian Variable Selection With Application to Functional Magnetic Resonance Imaging
• Regularization and Variable Selection Via the Elastic Net
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Incorporating spatial structure into inclusion probabilities for Bayesian variable selection in generalized linear models with the spike-and-slab elastic net