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The intrinsic bayes factor for model selection and prediction

Publication ,  Journal Article
Berger, JO; Pericchi, LR
Published in: Journal of the American Statistical Association
March 1, 1996

In the Bayesian approach to model selection or hypothesis testing with models or hypotheses of differing dimensions, it is typically not possible to utilize standard noninformative (or default) prior distributions. This has led Bayesians to use conventional proper prior distributions or crude approximations to Bayes factors. In this article we introduce a new criterion called the intrinsic Bayes factor, which is fully automatic in the sense of requiring only standard noninformative priors for its computation and yet seems to correspond to very reasonable actual Bayes factors. The criterion can be used for nested or nonnested models and for multiple model comparison and prediction. From another perspective, the development suggests a general definition of a “reference prior” for model comparison. Copyright 1996 Taylor & Francis Group, LLC.

Duke Scholars

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

March 1, 1996

Volume

91

Issue

433

Start / End Page

109 / 122

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Berger, J. O., & Pericchi, L. R. (1996). The intrinsic bayes factor for model selection and prediction. Journal of the American Statistical Association, 91(433), 109–122. https://doi.org/10.1080/01621459.1996.10476668
Berger, J. O., and L. R. Pericchi. “The intrinsic bayes factor for model selection and prediction.” Journal of the American Statistical Association 91, no. 433 (March 1, 1996): 109–22. https://doi.org/10.1080/01621459.1996.10476668.
Berger JO, Pericchi LR. The intrinsic bayes factor for model selection and prediction. Journal of the American Statistical Association. 1996 Mar 1;91(433):109–22.
Berger, J. O., and L. R. Pericchi. “The intrinsic bayes factor for model selection and prediction.” Journal of the American Statistical Association, vol. 91, no. 433, Mar. 1996, pp. 109–22. Scopus, doi:10.1080/01621459.1996.10476668.
Berger JO, Pericchi LR. The intrinsic bayes factor for model selection and prediction. Journal of the American Statistical Association. 1996 Mar 1;91(433):109–122.

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

March 1, 1996

Volume

91

Issue

433

Start / End Page

109 / 122

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics