GENERALIZED DOUBLE PARETO SHRINKAGE.
Journal Article (Journal Article)
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 t -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.
Full Text
Duke Authors
Cited Authors
- Armagan, A; Dunson, DB; Lee, J
Published Date
- January 2013
Published In
Volume / Issue
- 23 / 1
Start / End Page
- 119 - 143
PubMed ID
- 24478567
Pubmed Central ID
- PMC3903426
Electronic International Standard Serial Number (EISSN)
- 1996-8507
International Standard Serial Number (ISSN)
- 1017-0405
Language
- eng