scientific article; zbMATH DE number 7255104

zbMath1499.62244MaRDI QIDQ4969130

Anirban Bhattacharya, James E. Johndrow, Paulo Orenstein

Publication date: 5 October 2020

Full work available at URL: https://jmlr.csail.mit.edu/papers/v21/19-536.html

Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.

zbMATH Keywords

perturbation theoryBayesian analysishigh-dimensional modelsshrinkage priorMCMC approximation

Mathematics Subject Classification ID

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

Related Items (16)

Bayesian Regression Using a Prior on the Model Fit: The R2-D2 Shrinkage Prior ⋮ Bayesian sparse convex clustering via global-local shrinkage priors ⋮ Geometric ergodicity of Gibbs samplers for the horseshoe and its regularized variants ⋮ Bayesian Approaches to Shrinkage and Sparse Estimation ⋮ Shrinkage with shrunken shoulders: Gibbs sampling shrinkage model posteriors with guaranteed convergence rates ⋮ A horseshoe mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging ⋮ Prior-Preconditioned Conjugate Gradient Method for Accelerated Gibbs Sampling in “Large n , Large p ” Bayesian Sparse Regression ⋮ Horseshoe Priors for Edge-Preserving Linear Bayesian Inversion ⋮ Unnamed Item ⋮ Trace class Markov chains for the normal-gamma Bayesian shrinkage model ⋮ Discussion ⋮ Rejoinder: ``Best subset, forward stepwise or Lasso? Analysis and recommendations based on extensive comparisons ⋮ Unnamed Item ⋮ Bayesian Bootstrap Spike-and-Slab LASSO ⋮ Bayesian frequentist bounds for machine learning and system identification ⋮ Sparse estimation in linear dynamic networks using the stable spline horseshoe prior

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Geometric ergodicity of the Bayesian Lasso
• The horseshoe estimator: posterior concentration around nearly black vectors
• Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
• Markov chains and stochastic stability
• Uncertainty quantification for the horseshoe (with discussion)
• Perturbation theory for Markov chains via Wasserstein distance
• Least angle regression. (With discussion)
• Batch means and spectral variance estimators in Markov chain Monte Carlo
• Adaptive posterior contraction rates for the horseshoe
• Geometric ergodicity for Bayesian shrinkage models
• Yet Another Look at Harris’ Ergodic Theorem for Markov Chains
• The horseshoe estimator for sparse signals
• The Bayesian Lasso
• Convergence Properties of Perturbed Markov Chains
• Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
• Bayesian Model Selection in High-Dimensional Settings
• The Bayesian Bridge
• Dirichlet–Laplace Priors for Optimal Shrinkage
• Sensitivity and convergence of uniformly ergodic Markov chains

This page was built for publication: