Asymptotic optimality of one-group shrinkage priors in sparse high-dimensional problems

DOI10.1214/16-BA1029zbMath1384.62087MaRDI QIDQ1699710

Arijit Chakrabarti, Prasenjit Ghosh

Publication date: 23 February 2018

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ba/1475266758

zbMATH Keywords

sparsityposterior contractionasymptotic minimaxityhorseshoe priorABOSone-group shrinkage priors

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Bayesian inference (62F15) Bayesian problems; characterization of Bayes procedures (62C10) Minimax procedures in statistical decision theory (62C20) Paired and multiple comparisons; multiple testing (62J15)

Related Items (8)

High-dimensional multivariate posterior consistency under global-local shrinkage priors ⋮ Horseshoe Regularisation for Machine Learning in Complex and Deep Models¹ ⋮ Neuronized Priors for Bayesian Sparse Linear Regression ⋮ Shrinkage with shrunken shoulders: Gibbs sampling shrinkage model posteriors with guaranteed convergence rates ⋮ Global-local shrinkage priors for asymptotic point and interval estimation of normal means under sparsity ⋮ Large-scale multiple hypothesis testing with the normal-beta prime prior ⋮ Lasso meets horseshoe: a survey ⋮ Revisiting Jeffreys’ Example: Bayes Test of the Normal Mean

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