A caveat on the Savage-Dickey density ratio: The case of computing Bayes factors for regression parameters

Heck, Daniel W.

DOI: https://doi.org/10.1111/bmsp.12150
URL: https://onlinelibrary.wiley.com/doi/full/10.1111/b...
Additional URL: https://www.researchgate.net/publication/328138950...
Document Type: Article
Year of publication: 2019
The title of a journal, publication series: British Journal of Mathematical and Statistical Psychology
Volume: 72
Issue number: 2
Page range: 316-333
Place of publication: Hoboken, NJ
Publishing house: Wiley
ISSN: 0007-1102 , 2044-8317
Related URLs:
Publication language: English
Institution: School of Social Sciences > Kognitive Psychologie (Seniorprofessur) (Erdfelder 2019-)
Außerfakultäre Einrichtungen > Graduate School of Economic and Social Sciences- CDSS (Social Sciences)
Subject: 150 Psychology
Abstract: The Savage–Dickey density ratio is a simple method for computing the Bayes factor for an equality constraint on one or more parameters of a statistical model. In regression analysis, this includes the important scenario of testing whether one or more of the covariates have an effect on the dependent variable. However, the Savage–Dickey ratio only provides the correct Bayes factor if the prior distribution of the nuisance parameters under the nested model is identical to the conditional prior under the full model given the equality constraint. This condition is violated for multiple regression models with a Jeffreys–Zellner–Siow prior, which is often used as a default prior in psychology. Besides linear regression models, the limitation of the Savage–Dickey ratio is especially relevant when analytical solutions for the Bayes factor are not available. This is the case for generalized linear models, non‐linear models, or cognitive process models with regression extensions. As a remedy, the correct Bayes factor can be computed using a generalized version of the Savage–Dickey density ratio.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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ORCID: Heck, Daniel W. ORCID: 0000-0002-6302-9252

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