Uncertainty Quantification for Modern High-Dimensional Regression via Scalable Bayesian Methods

DOI10.1080/10618600.2018.1482767OpenAlexW2810822878WikidataQ129629607 ScholiaQ129629607MaRDI QIDQ3391194

Li-Yuan Zhang, Doug Sparks, Kshitij Khare, Bala Rajaratnam

Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2018.1482767

zbMATH Keywords

geometric ergodicityuncertainty quantificationhigh-dimensional regressionscale mixture of normalsBayesian shrinkageHilbert-Schmidt operator

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

Fast Markov Chain Monte Carlo for High-Dimensional Bayesian Regression Models With Shrinkage Priors ⋮ Estimating accuracy of the MCMC variance estimator: asymptotic normality for batch means estimators ⋮ Bayesian Approaches to Shrinkage and Sparse Estimation ⋮ Asynchronous and Distributed Data Augmentation for Massive Data Settings

Uses Software

• UCI-ml
• CODA
• BayesDA
• flare

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