Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11222-011-9279-3
Title: The predictive Lasso
Authors: Tran, M.-N.
Nott, D.J. 
Leng, C. 
Keywords: Generalized linear models
Kullback-Leibler divergence
Lasso
Optimal prediction
Variable selection
Issue Date: Sep-2012
Citation: Tran, M.-N., Nott, D.J., Leng, C. (2012-09). The predictive Lasso. Statistics and Computing 22 (5) : 1069-1084. ScholarBank@NUS Repository. https://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. © 2011 Springer Science+Business Media, LLC.
Source Title: Statistics and Computing
URI: http://scholarbank.nus.edu.sg/handle/10635/105428
ISSN: 09603174
DOI: 10.1007/s11222-011-9279-3
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