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.
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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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