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Avoiding prior–data conflict in regression models via mixture priors

Egidi L.
•
Pauli F.
•
Torelli N.
2022
  • journal article

Periodico
CANADIAN JOURNAL OF STATISTICS
Abstract
The Bayesian model consists of the prior–likelihood pair. A prior–data conflict arises whenever the prior allocates most of its mass to regions of the parameter space where the likelihood is relatively low. Once a prior–data conflict is diagnosed, what to do next is a hard question to answer. We propose an automatic prior elicitation that involves a two-component mixture of a diffuse and an informative prior distribution that favours the first component if a conflict emerges. Using various examples, we show that these mixture priors can be useful in regression models as a device for regularizing the estimates and retrieving useful inferential conclusions.
DOI
10.1002/cjs.11637
WOS
WOS:000678670000001
Archivio
http://hdl.handle.net/11368/2993048
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85111526171
https://onlinelibrary.wiley.com/doi/10.1002/cjs.11637
Diritti
open access
FVG url
https://arts.units.it/bitstream/11368/2993048/4/Egidi_Avoiding prior data conflict in regression models via mixture priors.pdf
Soggetti
  • Bayesian model

  • generative model

  • mixture prior

  • prior–data conflict

  • regression

Visualizzazioni
2
Data di acquisizione
Apr 19, 2024
Vedi dettagli
google-scholar
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