Dirichlet-Laplace Priors for Optimal Shrinkage

• 成果类型: Article
• 署名作者: Bhattacharya, Anirban; Pati, Debdeep; Pillai, Natesh S.; Dunson, David B.
• 署名单位: Texas A&M University System; Texas A&M University College Station; State University System of Florida; Florida State University; Harvard University; Duke University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.960967
• 发表日期: 2015
• 页码: 1479-1490
• 关键词: empirical-bayes; variable-selection; sparse sequences; scale mixtures; linear-models; regression; Lasso; distributions; needles; straw

摘要

Penalized regression methods, such as L-1 regularization, 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.