Bayesian variable selection for linear regression with the κ-G priors
Loading...
Date
2022
Authors
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky
Altmetrics
Abstract
In this paper, we propose a method that balances between variable se- lection and variable shrinkage in linear regression. A diagonal matrix G is injected to the covariance matrix of prior distribution of the coefficient vector β, with each gj , bounded between 0 and 1, on the diagonal serving as a stabilizer of the corre- sponding βj . Mathematically, a gj value close to 0 indicates that the βj is nonzero, and hence the corresponding variable should be selected, whereas the value of gj close to 1 indicates otherwise. We prove this property under orthogonality. Com- putationally, the proposed method is easy to fit using automated programs such as JAGS. We provide three examples to verify the capability of this methodology in variable selection and shrinkage.
Description
Citation
Mathematics for Applications. 2022 vol. 11, č. 2, s. 143-154. ISSN 1805-3629
http://ma.fme.vutbr.cz/archiv/11_2/ma_11_2_ma_fokoue_final.pdf
http://ma.fme.vutbr.cz/archiv/11_2/ma_11_2_ma_fokoue_final.pdf
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en
Study field
Comittee
Date of acceptance
Defence
Result of defence
Document licence
© Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky