Generalized double Pareto shrinkage
Abstract
We propose a generalized double Pareto prior for Bayesian shrinkage estimation and inferences in linear models. The prior can be obtained via a scale mixture of Laplace or normal distributions, forming a bridge between the Laplace and Normal-Jeffreys' priors. While it has a spike at zero like the Laplace density, it also has a Student's -like tail behavior. Bayesian computation is straightforward via a simple Gibbs sampling algorithm. We investigate the properties of the maximum a posteriori estimator, as sparse estimation plays an important role in many problems, reveal connections with some well-established regularization procedures, and show some asymptotic results. The performance of the prior is tested through simulations and an application.
Cite
@article{arxiv.1104.0861,
title = {Generalized double Pareto shrinkage},
author = {Artin Armagan and David Dunson and Jaeyong Lee},
journal= {arXiv preprint arXiv:1104.0861},
year = {2015}
}