Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10463-013-0429-6
Title: Bayesian adaptive Lasso
Authors: Leng, C. 
Tran, M.-N.
Nott, D. 
Keywords: Bayesian Lasso
Gibbs sampler
Lasso
Scale mixture of normals
Variable selection
Issue Date: 2014
Citation: Leng, 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
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. © 2013 The Institute of Statistical Mathematics, Tokyo.
Source Title: Annals of the Institute of Statistical Mathematics
URI: http://scholarbank.nus.edu.sg/handle/10635/105038
ISSN: 15729052
DOI: 10.1007/s10463-013-0429-6
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