We propose the Bayesian adaptive Lasso (BaLasso) for variable selection and coefficient
estimation in linear regression. The BaLasso is adaptive to the signal level by adopting …

The lasso is a popular technique for simultaneous estimation and variable selection. Lasso
variable selection has been shown to be consistent under certain conditions. In this work we …

The adaptive Lasso is a commonly applied penalty for variable selection in regression
modeling. Like all penalties though, its performance depends critically on the choice of the …

Despite the wide adoption of spike-and-slab methodology for Bayesian variable selection,
its potential for penalized likelihood estimation has largely been overlooked. In this article …

We propose a Bayesian implementation of the lasso regression that accomplishes both
shrinkage and variable selection. We focus on the appropriate specification for the …

The lasso (Tibshirani, 1996) has sparked interest in the use of penalization of the log-
likelihood for variable selection, as well as shrinkage. Recently, there have been attempts to …

Penalized regression methods for simultaneous variable selection and coefficient
estimation, especially those based on the lasso of Tibshirani (1996), have received a great …

The Lasso estimate for linear regression parameters can be interpreted as a Bayesian
posterior mode estimate when the regression parameters have independent Laplace (ie …

We propose an empirical Bayes method for variable selection and coefficient estimation in
linear regression models. The method is based on a particular hierarchical Bayes …

Park and Casella (2008) provided the Bayesian lasso for linear models by assigning scale
mixture of normal (SMN) priors on the parameters and independent exponential priors on …