 Dirichlet--Laplace Priors for Optimal ShrinkageAnirban Bhattacharya, Debdeep Pati, Natesh S. Pillai and David B. Dunson Journal of the American Statistical Association, 2015, vol. 110, issue 512, 1479-1490 Abstract: 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. Date: 2015 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:110:y:2015:i:512:p:1479-1490 Ordering information: This journal article can be ordered from DOI: 10.1080/01621459.2014.960967 Access Statistics for this article Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson More articles in Journal of the American Statistical Association from Taylor & Francis Journals |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.