Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10463-013-0429-6
DC FieldValue
dc.titleBayesian adaptive Lasso
dc.contributor.authorLeng, C.
dc.contributor.authorTran, M.-N.
dc.contributor.authorNott, D.
dc.date.accessioned2014-10-28T05:10:31Z
dc.date.available2014-10-28T05:10:31Z
dc.date.issued2014
dc.identifier.citationLeng, C., Tran, M.-N., Nott, D. (2014). Bayesian adaptive Lasso. Annals of the Institute of Statistical Mathematics 66 (2) : 221-244. ScholarBank@NUS Repository. https://doi.org/10.1007/s10463-013-0429-6
dc.identifier.issn15729052
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105038
dc.description.abstractWe propose the Bayesian adaptive Lasso (BaLasso) for variable selection and coefficient estimation in linear regression. The BaLasso is adaptive to the signal level by adopting different shrinkage for different coefficients. Furthermore, we provide a model selection machinery for the BaLasso by assessing the posterior conditional mode estimates, motivated by the hierarchical Bayesian interpretation of the Lasso. Our formulation also permits prediction using a model averaging strategy. We discuss other variants of this new approach and provide a unified framework for variable selection using flexible penalties. Empirical evidence of the attractiveness of the method is demonstrated via extensive simulation studies and data analysis. © 2013 The Institute of Statistical Mathematics, Tokyo.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s10463-013-0429-6
dc.sourceScopus
dc.subjectBayesian Lasso
dc.subjectGibbs sampler
dc.subjectLasso
dc.subjectScale mixture of normals
dc.subjectVariable selection
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1007/s10463-013-0429-6
dc.description.sourcetitleAnnals of the Institute of Statistical Mathematics
dc.description.volume66
dc.description.issue2
dc.description.page221-244
dc.description.codenAISXA
dc.identifier.isiut000332454200001
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