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GENERALIZED DOUBLE PARETO SHRINKAGE.

Publication ,  Journal Article
Armagan, A; Dunson, DB; Lee, J
Published in: Statistica Sinica
January 2013

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

Published In

Statistica Sinica

EISSN

1996-8507

ISSN

1017-0405

Publication Date

January 2013

Volume

23

Issue

1

Start / End Page

119 / 143

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0801 Artificial Intelligence and Image Processing
  • 0199 Other Mathematical Sciences
  • 0104 Statistics
 

Citation

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Armagan, A., Dunson, D. B., & Lee, J. (2013). GENERALIZED DOUBLE PARETO SHRINKAGE. Statistica Sinica, 23(1), 119–143.
Armagan, Artin, David B. Dunson, and Jaeyong Lee. “GENERALIZED DOUBLE PARETO SHRINKAGE.Statistica Sinica 23, no. 1 (January 2013): 119–43.
Armagan A, Dunson DB, Lee J. GENERALIZED DOUBLE PARETO SHRINKAGE. Statistica Sinica. 2013 Jan;23(1):119–43.
Armagan, Artin, et al. “GENERALIZED DOUBLE PARETO SHRINKAGE.Statistica Sinica, vol. 23, no. 1, Jan. 2013, pp. 119–43.
Armagan A, Dunson DB, Lee J. GENERALIZED DOUBLE PARETO SHRINKAGE. Statistica Sinica. 2013 Jan;23(1):119–143.

Published In

Statistica Sinica

EISSN

1996-8507

ISSN

1017-0405

Publication Date

January 2013

Volume

23

Issue

1

Start / End Page

119 / 143

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0801 Artificial Intelligence and Image Processing
  • 0199 Other Mathematical Sciences
  • 0104 Statistics