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Leng, Chenlei, Tran, Minh-Ngoc and Nott, David (2014) Bayesian adaptive Lasso. Annals of the Institute of Statistical Mathematics, Volume 66 (Number 2). pp. 221-244. doi:10.1007/s10463-013-0429-6
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Official URL: http://dx.doi.org/10.1007/s10463-013-0429-6
Abstract
We 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.
| Item Type: | Journal Article | ||||||||
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| Divisions: | Faculty of Science > Statistics | ||||||||
| Journal or Publication Title: | Annals of the Institute of Statistical Mathematics | ||||||||
| Publisher: | Springer Link | ||||||||
| ISSN: | 0020-3157 | ||||||||
| Official Date: | April 2014 | ||||||||
| Dates: |
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| Volume: | Volume 66 | ||||||||
| Number: | Number 2 | ||||||||
| Number of Pages: | 23 | ||||||||
| Page Range: | pp. 221-244 | ||||||||
| DOI: | 10.1007/s10463-013-0429-6 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access |
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