Global-local shrinkage priors for asymptotic point and interval estimation of normal means under sparsity

DOI10.1007/S13171-023-00315-9arXiv2310.18898OpenAlexW4386542693MaRDI QIDQ6123488

Malay Ghosh, Zikun Qin

Publication date: 4 March 2024

Published in: Sankhyā. Series A (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2310.18898

zbMATH Keywords

minimaxconcentration inequalitiesexact ratebeta prime priorsexponential-inverse-gamma

Mathematics Subject Classification ID

Bayesian inference (62F15)

Cites Work

• Unnamed Item
• Conditions for posterior contraction in the sparse normal means problem
• The horseshoe estimator: posterior concentration around nearly black vectors
• Hierarchical shrinkage priors for regression models
• Asymptotic optimality of one-group shrinkage priors in sparse high-dimensional problems
• Uncertainty quantification for the horseshoe (with discussion)
• Needles and straw in a haystack: posterior concentration for possibly sparse sequences
• The horseshoe estimator for sparse signals
• Dirichlet–Laplace Priors for Optimal Shrinkage

This page was built for publication: Global-local shrinkage priors for asymptotic point and interval estimation of normal means under sparsity