title: Dirichlet-Laplace priors for optimal shrinkage.
abstract: Penalized regression methods, such asL1regularization, are routinely used in high-dimensional applications, and there is a rich literature on optimality properties under sparsity assumptions. In the Bayesian paradigm, sparsity is routinely induced through two-component mixture priors having a probability mass at zero, but such priors encounter daunting computational problems in high dimensions. This has motivated continuous shrinkage priors, which can be expressed as global-local scale mixtures of Gaussians, facilitating computation. In contrast to the frequentist literature, little is known about the properties of such priors and the convergence and concentration of the corresponding posterior distribution. In this article, we propose a new class of Dirichlet-Laplace priors, which possess optimal posterior concentration and lead to efficient posterior computation. Finite sample performance of Dirichlet-Laplace priors relative to alternatives is assessed in simulated and real data examples.
authors.affiliation: Department of Statistics, Texas A&M University, Department of Statistics, Florida State University, Department of Statistics, Harvard University, Department of Statistical Science, Duke University.
authors.forename: Anirban
authors.initials: A
authors.lastname: Bhattacharya
authors.affiliation: Department of Statistics, Texas A&M University, Department of Statistics, Florida State University, Department of Statistics, Harvard University, Department of Statistical Science, Duke University.
authors.forename: Debdeep
authors.initials: D
authors.lastname: Pati
authors.affiliation: Department of Statistics, Texas A&M University, Department of Statistics, Florida State University, Department of Statistics, Harvard University, Department of Statistical Science, Duke University.
authors.forename: Natesh S
authors.initials: NS
authors.lastname: Pillai
authors.affiliation: Department of Statistics, Texas A&M University, Department of Statistics, Florida State University, Department of Statistics, Harvard University, Department of Statistical Science, Duke University.
authors.forename: David B
authors.initials: DB
authors.lastname: Dunson
keywords: Bayesian; Convergence rate; High dimensional; L1; Lasso; Penalized regression; Regularization; Shrinkage prior