with the Lasso algorithm in the linear least squared settings. We show that the fitted transformation matrix is close to …

approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method … for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes … weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets …

with Bayesian Markov Chain Monte Carlo. The resulting model is equivalent to the frequentist lasso procedure. A … covariates is provided by the approach. An implementation of the lasso procedure for binary quantile regression models is …

We propose the Bayesian adaptive Lasso (BaLasso) for variable selection and coefficient estimation in linear regression … hierarchical Bayesian interpretation of the Lasso. Our formulation also permits prediction using a model averaging strategy. We …

correlations. We then introduce a two step LASSO procedure, called NETS, to estimate high dimensional sparse Long Run Partial …