Sub-optimality of some continuous shrinkage priors
Journal Article (Journal Article)
Two-component mixture priors provide a traditional way to induce sparsity in high-dimensional Bayes models. However, several aspects of such a prior, including computational complexities in high-dimensions, interpretation of exact zeros and non-sparse posterior summaries under standard loss functions, have motivated an amazing variety of continuous shrinkage priors, which can be expressed as global–local scale mixtures of Gaussians. Interestingly, we demonstrate that many commonly used shrinkage priors, including the Bayesian Lasso, do not have adequate posterior concentration in high-dimensional settings.
Full Text
Duke Authors
Cited Authors
- Bhattacharya, A; Dunson, DB; Pati, D; Pillai, NS
Published Date
- December 1, 2016
Published In
Volume / Issue
- 126 / 12
Start / End Page
- 3828 - 3842
International Standard Serial Number (ISSN)
- 0304-4149
Digital Object Identifier (DOI)
- 10.1016/j.spa.2016.08.007
Citation Source
- Scopus