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On model selection by quasi-Bayesian predictive densities

  • University of Cincinnati

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A Bayesian procedure for model selection recently proposed by Aitkin [1] drew the attention by its heretical (from a Bayesian point of view) feature to validate a model by data that have already been used to estimate the model parameters. In the paper we discuss some of the issues that this procedure was meant to resolve. We also show how the same issues could be resolved without violating the Bayesian paradigm. Examples and computer simulations are provided in which the Bayesian and quasi-Bayesian approaches are compared. It is shown that in the case of linear models the quasi- Bayesian methods yield poor results.

Original languageEnglish
Title of host publication1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2372-2375
Number of pages4
ISBN (Electronic)0780305930
DOIs
StatePublished - 1992
Event1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992 - San Diego, United States
Duration: May 10 1992May 13 1992

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume5
ISSN (Print)0271-4310

Conference

Conference1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992
Country/TerritoryUnited States
CitySan Diego
Period05/10/9205/13/92

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