314
Views
0
CrossRef citations to date
0
Altmetric
Articles

Power-expected-posterior prior Bayes factor consistency for nested linear models with increasing dimensions

ORCID Icon, &
Pages 162-171 | Received 13 May 2019, Accepted 14 Jan 2020, Published online: 30 Jan 2020
 

Abstract

The power-expected-posterior prior is used in this paper for comparing nested linear models. The asymptotic behaviour of the method is investigated for different values of the power parameter of the prior. Focus is given on the consistency of the Bayes factor of comparing the full model Mp versus a generic submodel M. In each case, we allow the true generating model to be either Mp or M and we keep the dimension of M fixed, while the dimension of Mp can be either fixed or (grow as) O(n), with n denoting the sample size.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

D. Fouskakis

D. Fouskakis is an Associate Professor in the Department of Mathematics, at the National Technical University of Athens, in Greece. He is also the Director of the Stats Lab at the same University. His research mostly focuses on Bayesian model and variable selection, on objective priors and on stochastic optimization methods.

J. K. Innocent

J. K. Innocent received a Ph.D in Mathematics at the University of Puerto Rico, Puerto Rico, USA in 2016. He is currently back to Haiti, where he teaches mathematical and Statistical courses at a university level. His main research areas are on Bayesian Statistics, Statistical Analysis, Biostatistics and Epidemiology.

L. Pericchi

L. Pericchi is a Full Professor in the Department of Mathematics of the University of Puerto Rico Rio Piedras, USA. He is also the Director of the Center of Biostatistics and Bioinformatics of the College of Natural Sciences. His research is in the Theory and Applications of Statistics, with emphasis in the Bayesian Approach.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.