The adaptive normal-hypergeometric-inverted-beta priors for sparse signals

DOI10.1080/00949655.2020.1815199OpenAlexW3084199469MaRDI QIDQ5033960

Xinyi Xu, Han-Jun Yu, Di Cao

Publication date: 24 February 2022

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

Full work available at URL: https://doi.org/10.1080/00949655.2020.1815199

zbMATH Keywords

sparsitysuper efficiencyglobal-local shrinkagetail robustnessadaptive normal-hypergeometric-inverted-beta prior

Mathematics Subject Classification ID

Bayesian inference (62F15) Statistics (62-XX)

Uses Software

• wmtsa

Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• The horseshoe estimator: posterior concentration around nearly black vectors
• Asymptotic properties of Bayes risk of a general class of shrinkage priors in multiple hypothesis testing under sparsity
• Asymptotic properties of Bayes risk for the horseshoe prior
• A robust generalized Bayes estimator and confidence region for a multivariate normal mean
• Estimation of the mean of a multivariate normal distribution
• The horseshoe+ estimator of ultra-sparse signals
• On the half-Cauchy prior for a global scale parameter
• The Bayesian elastic net
• Inference with normal-gamma prior distributions in regression problems
• Information-theoretic asymptotics of Bayes methods
• The horseshoe estimator for sparse signals
• Bayesian Variable Selection in Linear Regression
• Flexible Empirical Bayes Estimation for Wavelets
• Generalized double Pareto shrinkage
• Regularization and Variable Selection Via the Elastic Net
• Dirichlet–Laplace Priors for Optimal Shrinkage
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Ridge Regression: Applications to Nonorthogonal Problems
• Proper Bayes Minimax Estimators of the Multivariate Normal Mean