Multivariate Bayesian Global–Local Shrinkage Methods for Regularisation in the High-Dimensional Linear Model

Multivariate Bayesian Global–Local Shrinkage Methods for Regularisation in the High-Dimensional Linear Model

This paper considers Bayesian regularisation using global–local shrinkage priors in the multivariate general linear model when there are many more explanatory variables than observations. We adopt priors’ structures used extensively in univariate problems (conjugate and non-conjugate with tail behav...

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Bibliographic Details
Main Authors: Valentina Mameli, Debora Slanzi, Jim E. Griffin, Philip J. Brown
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Mathematics
Subjects:
seemingly unrelated multivariate normal regression
structured priors
global–local priors
exponential-tailed and polynomial-tailed priors
drug discovery
chemometrics
Online Access:https://www.mdpi.com/2227-7390/13/11/1812
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