Dimitris Fouskakis
Variations of power-expected-posterior priors in normal regression models
Fouskakis, Dimitris; Ntzoufras, Ioannis; Perrakis, Konstantinos
Authors
Abstract
The power-expected-posterior (PEP) prior is an objective prior for Gaussian linear models, which leads to consistent model selection inference, under the M-closed scenario, and tends to favour parsimonious models. Recently, two new forms of the PEP prior were proposed which generalize its applicability to a wider range of models. The properties of these two PEP variants within the context of the normal linear model are examined thoroughly, focusing on the prior dispersion and on the consistency of the induced model selection procedure. Results show that both PEP variants have larger variances than the unit-information -prior and that they are M-closed consistent as the limiting behaviour of the corresponding marginal likelihoods matches that of the BIC. The consistency under the M-open case, using three different model misspecification scenarios is further investigated.
Citation
Fouskakis, D., Ntzoufras, I., & Perrakis, K. (2020). Variations of power-expected-posterior priors in normal regression models. Computational Statistics & Data Analysis, 143, Article 106836. https://doi.org/10.1016/j.csda.2019.106836
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 4, 2019 |
Online Publication Date | Sep 12, 2019 |
Publication Date | Mar 1, 2020 |
Deposit Date | Sep 26, 2019 |
Publicly Available Date | Sep 12, 2020 |
Journal | Computational Statistics & Data Analysis |
Print ISSN | 0167-9473 |
Electronic ISSN | 1872-7352 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 143 |
Article Number | 106836 |
DOI | https://doi.org/10.1016/j.csda.2019.106836 |
Public URL | https://durham-repository.worktribe.com/output/1290369 |
Related Public URLs | https://arxiv.org/pdf/1609.06926.pdf |
Files
Accepted Journal Article
(1.7 Mb)
PDF
Copyright Statement
© 2019 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
You might also like
A Bayesian approach for modeling origin-destination matrices
(2011)
Presentation / Conference Contribution
Quantifying input-uncertainty in traffic assignment models
(2012)
Presentation / Conference Contribution
Poisson mixture regression for Bayesian inference on large over-dispersed transportation origin-destination matrices
(2012)
Presentation / Conference Contribution
Regularized joint mixture models
(2023)
Journal Article
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search