In this work, we propose a Bayesian implementation of the Lasso regression that accomplishes both shrinkage and variable selection. We focus on the appropriate specification for the shrinkage parameter λ through Bayes factors that evaluate the inclusion of each covariate in the model formulation. We associate this parameter with the values of Pearson and partial correlation at the limits between significance and insignificance as defined by Bayes factors. By this way, a meaningful interpretation of λ is achieved that leads to a simple specification of this parameter which is of prominent importance in Lasso literature.
Keywords: Bayes factors; MCMC; Partial Correlation; Pearson Correlation; Shrinkage; Benchmark and Threshold Correlations.
Download:
First version: 16/3/2011 available here.
Finally published version: [13/1/2012] available here.
R functions for BVLS are available here (with a small pdf help file and code for running the examples of the paper); For instructions see here. [corrected and updated 7/2/2012].
«Η παρούσα έρευνα χρηματοδοτήθηκε από τους πόρους του Ειδικού Λογαριασμού Κονδυλίων Έρευνας του Οικονομικού Πανεπιστημίου Αθηνών».
«This research was funded by the Research Centre of the Athens University of Economics and Business».
