Nearly optimal Bayesian shrinkage for high-dimensional regression (Q2683046)

scientific article; zbMATH DE number 7649255

Language	Label	Description	Also known as
English	Nearly optimal Bayesian shrinkage for high-dimensional regression	scientific article; zbMATH DE number 7649255

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instance of

scholarly article

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title

Nearly optimal Bayesian shrinkage for high-dimensional regression (English)

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Science China. Mathematics

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publication date

3 February 2023

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full work available at URL

https://arxiv.org/abs/1712.08964

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zbMATH Keywords

Bayesian variable selection

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absolutely continuous shrinkage prior

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heavy tail

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posterior consistency

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high-dimensional inference

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describes a project that uses

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Generalized double Pareto shrinkage

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Posterior consistency in linear models under shrinkage priors

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Q5004047

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Q4510963

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Empirical Bayes oracle uncertainty quantification for regression

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Prediction risk for the horseshoe regression

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Dirichlet–Laplace Priors for Optimal Shrinkage

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Bernstein-von Mises theorems for Gaussian regression with increasing number of regressors

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Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection

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The horseshoe estimator for sparse signals

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Bayesian linear regression with sparse priors

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Needles and straw in a haystack: posterior concentration for possibly sparse sequences

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Extended Bayesian information criteria for model selection with large model spaces

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High-dimensional inference: confidence intervals, $p$-values and R-software \texttt{hdi}

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Large-Scale Simultaneous Hypothesis Testing

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Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

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A general framework for Bayes structured linear models

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Asymptotic normality of posterior distributions in high-dimensional linear models

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Convergence rates of posterior distributions.

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Convergence rates of posterior distributions for non iid observations

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Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods

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Bayesian hyper-Lassos with non-convex penalization

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Structuring shrinkage: some correlated priors for regression

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Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective

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Bayesian lasso regression

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Q3003685

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Spike and slab variable selection: frequentist and Bayesian strategies

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Bayesian variable selection for high dimensional generalized linear models: convergence rates of the fitted densities

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Bayesian Model Selection in High-Dimensional Settings

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Misspecification in infinite-dimensional Bayesian statistics

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Variable selection using shrinkage priors

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Q5502121

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Bayesian Subset Modeling for High-Dimensional Generalized Linear Models

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Estimating the false discovery rate using the stochastic approximation algorithm

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A significance test for the lasso

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Empirical Bayes posterior concentration in sparse high-dimensional linear models

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Bayesian variable selection with shrinking and diffusing priors

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Q3172405

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The Bayesian Lasso

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Posterior contraction in sparse Bayesian factor models for massive covariance matrices

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Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls

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The Spike-and-Slab LASSO

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Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem

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Bayesian shrinkage towards sharp minimaxity

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High-Dimensional Variable Selection With Reciprocal <i>L</i><sub>1</sub>-Regularization

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A Split-and-Merge Bayesian Variable Selection Approach for Ultrahigh Dimensional Regression

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Q4864293

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On asymptotically optimal confidence regions and tests for high-dimensional models

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The horseshoe estimator: posterior concentration around nearly black vectors

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Uncertainty quantification for the horseshoe (with discussion)

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Adaptive posterior contraction rates for the horseshoe

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Weak convergence and empirical processes. With applications to statistics

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Sparse Bayesian Additive Nonparametric Regression with Application to Health Effects of Pesticides Mixtures

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On the computational complexity of high-dimensional Bayesian variable selection

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Nearly unbiased variable selection under minimax concave penalty

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The sparsity and bias of the LASSO selection in high-dimensional linear regression

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Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models

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Regularization and Variable Selection Via the Elastic Net

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A complete proof of universal inequalities for the distribution function of the binomial law

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Identifiers

Mathematics Subject Classification ID

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10.1007/S11425-020-1912-6

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Sitelinks

Mathematics(1 entry)

mardi Publication:2683046