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The predictive Lasso
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Tran, Minh-Ngoc, Nott, David J. and Leng, Chenlei (2012) The predictive Lasso. Statistics and Computing, Volume 22 (Number 5). pp. 1069-1084. doi:10.1007/s11222-011-9279-3 ISSN 0960-3174.
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Official URL: http://dx.doi.org/10.1007/s11222-011-9279-3
Abstract
We propose a shrinkage procedure for simultaneous variable selection and estimation in generalized linear models (GLMs) with an explicit predictive motivation. The procedure estimates the coefficients by minimizing the Kullback-Leibler divergence of a set of predictive distributions to the corresponding predictive distributions for the full model, subject to an l 1 constraint on the coefficient vector. This results in selection of a parsimonious model with similar predictive performance to the full model. Thanks to its similar form to the original Lasso problem for GLMs, our procedure can benefit from available l 1-regularization path algorithms. Simulation studies and real data examples confirm the efficiency of our method in terms of predictive performance on future observations.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
Journal or Publication Title: | Statistics and Computing | ||||||||
Publisher: | Springer | ||||||||
ISSN: | 0960-3174 | ||||||||
Official Date: | 21 September 2012 | ||||||||
Dates: |
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Volume: | Volume 22 | ||||||||
Number: | Number 5 | ||||||||
Number of Pages: | 15 | ||||||||
Page Range: | pp. 1069-1084 | ||||||||
DOI: | 10.1007/s11222-011-9279-3 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access |
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