Lamnisos, Demetris, Griffin, Jim E., Steel, Mark F.J. (2013) Adaptive Monte Carlo for Bayesian Variable Selection in Regression Models. Journal of Computational and Graphical Statistics, 22 (3). pp. 729-748. ISSN 1061-8600. (doi:10.1080/10618600.2012.694756) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:41222)
| The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication) | |
| Official URL: http://amstat.tandfonline.com/doi/pdf/10.1080/1061... |
Abstract
This article describes methods for efficient posterior simulation for Bayesian variable selection in generalized linear models with many regressors but few observations. The algorithms use a proposal on model space that contains a tuneable parameter. An adaptive approach to choosing this tuning parameter is described that allows automatic, efficient computation in these models. The method is applied to examples from normal linear and probit regression. Relevant code and datasets are posted online as supplementary materials.
| Item Type: | Article |
|---|---|
| DOI/Identification number: | 10.1080/10618600.2012.694756 |
| Subjects: | Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics |
| Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
| Depositing User: | Jim Griffin |
| Date Deposited: | 29 May 2014 15:41 UTC |
| Last Modified: | 16 Feb 2021 12:53 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/41222 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Griffin, Jim E..
| Creator's ORCID: |
 https://orcid.org/0000-0002-4828-7368
|
|---|---|
| CReDIT Contributor Roles: |
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):
Altmetric
Altmetric
