A note on consistency of Bayesian high-dimensional variable selection under a default prior

DOI10.1002/sta4.282MaRDI QIDQ6541571

Gyuhyeong Goh, Min Hua

Publication date: 19 May 2024

Published in: Stat (Search for Journal in Brave)

zbMATH Keywords

Bayesian asymptoticsZellner's \(g\)-priorhigh-dimensional linear regressionmodel selection consistencylarge \(p\) small \(n\) problem

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Sure independence screening in generalized linear models with NP-dimensionality
• Necessary and sufficient conditions for high-dimensional posterior consistency under \(g\)-priors
• On consistency and optimality of Bayesian variable selection based on \(g\)-prior in normal linear regression models
• Informative \(g\)-priors for logistic regression
• Consistency of objective Bayes factors as the model dimension grows
• The risk inflation criterion for multiple regression
• Empirical Bayes vs. fully Bayes variable selection
• Calibration and empirical Bayes variable selection
• Mixtures ofg-Priors in Generalized Linear Models
• A STATISTICAL PARADOX
• A comment on D. V. Lindley's statistical paradox
• Mixtures of g Priors for Bayesian Variable Selection
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion
• Benchmark priors for Bayesian model averaging.

This page was built for publication: A note on consistency of Bayesian high-dimensional variable selection under a default prior