We propose a full Bayesian treatment of the Group-Lasso, extending the standard Bayesian Lasso, using hierarchical expansion.

While the Bayesian Group-Lasso is applicable for many different likelihood models, the focus of this paper is on. Poisson models for contingency tables. In ...

Raman [10] proposed the Bayesian version of group Lasso method, applied it to contingency tables, and proved its stability and efficiency. Ibrahim [11] ...

A full Bayesian treatment of the Group-Lasso is proposed, extending the standard Bayesian Lasso, using hierarchical expansion, which is then applied to ...

While the Bayesian Group-Lasso is applicable for many different likelihood models, the focus of this paper is on. Poisson models for contingency tables. In ...

Categorical variables - leading to sparsity in groups. ▫. Count data – Frequently encountered in medical applications. ▫ Meaningful Variance estimates ...

Mar 22, 2012 · To overcome such problems, we propose a full Bayesian treatment of the Group-Lasso, extending the standard Bayesian Lasso, using hierarchical ...

Aug 26, 2009 · Group-Lasso estimators, useful in many applications, suffer from lack of meaningful variance estimates for regression coefficients.

Abstract. The paper revisits the Bayesian group lasso and uses spike and slab priors for group variable selection. In the process, the connection of our ...

Abstract. The paper revisits the Bayesian group lasso and uses spike and slab priors for group variable selection. In the process, the connection of our ...