GENERALIZED DOUBLE PARETO SHRINKAGE.
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.
Duke Scholars
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Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0801 Artificial Intelligence and Image Processing
- 0199 Other Mathematical Sciences
- 0104 Statistics
Citation
Published In
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0801 Artificial Intelligence and Image Processing
- 0199 Other Mathematical Sciences
- 0104 Statistics