Scalable Bayesian Model Averaging Through Local Information Propagation

Li Ma

Journal of the American Statistical Association, 2015, vol. 110, issue 510, 795-809

Abstract: This article shows that a probabilistic version of the classical forward-stepwise variable inclusion procedure can serve as a general data-augmentation scheme for model space distributions in (generalized) linear models. This latent variable representation takes the form of a Markov process, thereby allowing information propagation algorithms to be applied for sampling from model space posteriors. In particular, We propose a sequential Monte Carlo method for achieving effective unbiased Bayesian model averaging in high-dimensional problems, using proposal distributions constructed using local information propagation. The method--called LIPS for local information propagation based sampling--is illustrated using real and simulated examples with dimensionality ranging from 15 to 1000, and its performance in estimating posterior inclusion probabilities and in out-of-sample prediction is compared to those of several other methods--namely, MCMC, BAS, iBMA, and LASSO. In addition, it is shown that the latent variable representation can also serve as a modeling tool for specifying model space priors that account for knowledge regarding model complexity and conditional inclusion relationships. Supplementary materials for this article are available online.

Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2014.980908 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:110:y:2015:i:510:p:795-809

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2014.980908

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().