Before revision: Regression shrinkage and variable selection are important concepts in high-dimensional statistics that allow the inference of robust models from large data sets. Bayesian methods achieve this by subjecting the model parameters to a prior distribution whose mass is centred around zero. In particular, the lasso and elastic net linear regression models employ a double-exponential distribution in their prior , which results in some maximum-likelihood regression coefficients being identically zero.
After revision: The lasso and elastic net linear regression models employ a double-exponential distribution in their prior , which results in some maximum-likelihood regression coefficients being identically zero.