Bayesian high-dimensional screening via MCMC

DOI10.1016/j.jspi.2014.07.002zbMath1307.62075arXiv1302.1154OpenAlexW2016133507MaRDI QIDQ466528

Zuofeng Shang, Ping Li

Publication date: 27 October 2014

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1302.1154

zbMATH Keywords

model selectionhigh-dimensionalityposterior consistencyconstrained blockwise Gibbs samplerfully Bayesian methodgeneralized hyper-\(g\) priorgeneralized Zellner-Siow priorsize-control prior on model space

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Bayesian inference (62F15)

Related Items (1)

High-dimensional Bayesian inference in nonparametric additive models

Uses Software

• BayesDA

Cites Work

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• Sure independence screening in generalized linear models with NP-dimensionality
• Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm
• A unified approach to model selection and sparse recovery using regularized least squares
• Mixtures of \(g\)-priors for Bayesian model averaging with economic applications
• Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
• High-dimensional variable selection
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Consistency of objective Bayes factors for nonnested linear models and increasing model dimension
• Consistency of objective Bayes factors as the model dimension grows
• Lasso-type recovery of sparse representations for high-dimensional data
• Consistency of Bayesian procedures for variable selection
• Nonparametric regression using Bayesian variable selection
• Approximations and consistency of Bayes factors as model dimension grows
• The risk inflation criterion for multiple regression
• Consistency of Bayesian linear model selection with a growing number of parameters
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models
• High-dimensional generalized linear models and the lasso
• Bayesian variable selection for high dimensional generalized linear models: convergence rates of the fitted densities
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Consistency of Bayes factors for intrinsic priors in normal linear models
• High-dimensional graphs and variable selection with the Lasso
• Calibration and empirical Bayes variable selection
• Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
• The Intrinsic Bayes Factor for Model Selection and Prediction
• Objective Bayesian model selection in Gaussian graphical models
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• Sequential Monte Carlo Samplers
• Adaptive sampling for Bayesian variable selection
• Mixtures of g Priors for Bayesian Variable Selection
• Multiple shrinkage and subset selection in wavelets
• Bayesian Wavelet Regression on Curves With Application to a Spectroscopic Calibration Problem
• Flexible Empirical Bayes Estimation for Wavelets
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Bayes Model Averaging with Selection of Regressors
• Bayesian Variable Selection and Regularization for Time–Frequency Surface Estimation
• Consistent High-Dimensional Bayesian Variable Selection via Penalized Credible Regions
• Likelihood-Based Selection and Sharp Parameter Estimation
• Bayesian Model Selection in High-Dimensional Settings
• Bayesian Variable Selection in Structured High-Dimensional Covariate Spaces With Applications in Genomics
• Empirical Bayes False Coverage Rate Controlling Confidence Intervals
• Benchmark priors for Bayesian model averaging.

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