Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#322549] of David B. Dunson


Papers Published

  1. Bhattacharya, A; Pati, D; Pillai, NS; Dunson, DB, Dirichlet-Laplace priors for optimal shrinkage., Journal of the American Statistical Association, vol. 110 no. 512 (December, 2015), pp. 1479-1490 [doi]
    (last updated on 2019/05/23)

    Penalized regression methods, such as L1 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.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320