High-dimensional posterior consistency of the Bayesian lasso

DOI10.1080/03610926.2014.966840zbMath1349.62077OpenAlexW2515967845MaRDI QIDQ2832662

Shibasish Dasgupta

Publication date: 11 November 2016

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2014.966840

zbMATH Keywords

orthogonal designposterior consistencyhigh-dimensional variable selectionBayesian Lasso

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Bayesian inference (62F15)

Cites Work

• The Adaptive Lasso and Its Oracle Properties
• One-step sparse estimates in nonconcave penalized likelihood models
• Asymptotics for Lasso-type estimators.
• Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences
• Inference with normal-gamma prior distributions in regression problems
• Posterior consistency in linear models under shrinkage priors
• The horseshoe estimator for sparse signals
• Mixtures of g Priors for Bayesian Variable Selection
• The Bayesian Lasso
• Bayesian lasso regression
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Efficient Empirical Bayes Variable Selection and Estimation in Linear Models

This page was built for publication: High-dimensional posterior consistency of the Bayesian lasso