Bayesian adaptive Lasso
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- 作者：Chenlei Leng (1) (2)
Minh-Ngoc Tran (3)
David Nott (1) - 关键词：Bayesian Lasso ; Gibbs sampler ; Lasso ; Scale mixture of normals ; Variable selection
- 刊名：Annals of the Institute of Statistical Mathematics
- 出版年：2014
- 出版时间：April 2014
- 年：2014
- 卷：66
- 期：2
- 页码：221-244
- 全文大小：432 KB
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Minh-Ngoc Tran (3)
David Nott (1)
1. Department of Statistics and Applied Probability, National University of Singapore, Singapore, 117546, Singapore
2. Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK
3. Australian School of Business, University of New South Wales, Sydney, NSW, 2052, Australia - ISSN：1572-9052
文摘
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.