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Bayesian global-local shrinkage methods for regularisation in the high dimension linear model

Griffin, JE; Brown, PJ; (2021) Bayesian global-local shrinkage methods for regularisation in the high dimension linear model. Chemometrics and Intelligent Laboratory Systems , 210 , Article 104255. 10.1016/j.chemolab.2021.104255. Green open access

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Abstract

This paper reviews global-local prior distributions for Bayesian inference in high-dimensional regression problems including important properties of priors and efficient Markov chain Monte Carlo methods for inference. A chemometric example in drug discovery is used to compare the predictive performance of these methods with popular methods such as Ridge and LASSO regression.

Type: Article
Title: Bayesian global-local shrinkage methods for regularisation in the high dimension linear model
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.chemolab.2021.104255
Publisher version: http://dx.doi.org/10.1016/j.chemolab.2021.104255
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Regularisation, Linear model, High dimensional, Fast algorithms, Drug discovery, Bayesian shrinkage priors
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10122901
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