Default Bayes factors for model selection in regression

Rouder, Jeffrey N. and Morey, Richard D. ORCID: https://orcid.org/0000-0001-9220-3179 2012. Default Bayes factors for model selection in regression. Multivariate Behavioral Research 47 (6) , pp. 877-903. 10.1080/00273171.2012.734737

Full text not available from this repository.

Abstract

In this article, we present a Bayes factor solution for inference in multiple regression. Bayes factors are principled measures of the relative evidence from data for various models or positions, including models that embed null hypotheses. In this regard, they may be used to state positive evidence for a lack of an effect, which is not possible in conventional significance testing. One obstacle to the adoption of Bayes factor in psychological science is a lack of guidance and software. Recently, Liang, Paulo, Molina, Clyde, and Berger (2008) OpenURL Cardiff University developed computationally attractive default Bayes factors for multiple regression designs. We provide a web applet for convenient computation and guidance and context for use of these priors. We discuss the interpretation and advantages of the advocated Bayes factor evidence measures.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology
Subjects:	B Philosophy. Psychology. Religion > BF Psychology
Publisher:	Teylor & Francis
ISSN:	0027-3171
Last Modified:	27 Oct 2022 10:07
URI:	https://orca.cardiff.ac.uk/id/eprint/68953

Citation Data

Cited 329 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item