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Bayesian adaptive Lasso

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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

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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
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:
DateEvent
April 2014Published
3 September 2013Available
4 July 2012Submitted
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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