2010/09/13 by Tran, Minh-Ngoc, Nott, David, Leng, Chenlei
#FOS: Computer and information sciences #Methodology (stat.ME)
paper · doi:10.48550/arxiv.1009.2302
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 l1 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 l1-regularization path algorithms. Simulation studies and real-data examples confirm the efficiency of our method in terms of predictive performance on future observations.