Statistical inference via conditional Bayesian posteriors in high-dimensional linear regression

DOI10.1214/23-EJS2113MaRDI QIDQ2689601

Teng Wu, Yun Yang, Naveen Naidu Narisetty

Publication date: 13 March 2023

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-17/issue-1/Statistical-inference-via-conditional-Bayesian-posteriors-in-high-dimensional-linear/10.1214/23-EJS2113.full

zbMATH Keywords

Bayesian inferencesparsityuncertainty quantificationBayesian regularizationhigh dimensional linear model

Mathematics Subject Classification ID

Linear regression; mixed models (62J05)

Uses Software

• BayesDA
• hdm
• MBSP

Cites Work

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• On asymptotically optimal confidence regions and tests for high-dimensional models
• Nearly unbiased variable selection under minimax concave penalty
• Nonparametric Bernstein-von Mises theorems in Gaussian white noise
• Bernstein-von Mises theorems for Gaussian regression with increasing number of regressors
• Bayesian linear regression with sparse priors
• High-dimensional multivariate posterior consistency under global-local shrinkage priors
• Uncertainty quantification for the horseshoe (with discussion)
• Regularization and confounding in linear regression for treatment effect estimation
• High-dimensional inference: confidence intervals, \(p\)-values and R-software \texttt{hdi}
• Asymptotics for Lasso-type estimators.
• The Bernstein-von Mises theorem under misspecification
• Empirical Bayes oracle uncertainty quantification for regression
• Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity
• Spike and slab empirical Bayes sparse credible sets
• Needles and straw in a haystack: robust confidence for possibly sparse sequences
• Spike and slab variable selection: frequentist and Bayesian strategies
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors
• Efficient Computing of Regression Diagnostics
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Inference on Treatment Effects after Selection among High-Dimensional Controls
• High-Dimensional Probability
• The Spike-and-Slab LASSO
• Calibrating general posterior credible regions
• Partial Correlation Estimation by Joint Sparse Regression Models
• A Convex Pseudolikelihood Framework for High Dimensional Partial Correlation Estimation with Convergence Guarantees
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models

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