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The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

The Intrinsic Bayes Factor for Model Selection and Prediction

Berger, James O., Pericchi, Luis R.·1996·Journal of the American Statistical Association
DOI 10.1080/01621459.1996.10476668
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Abstract

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.

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Research works printed but not linked: 45

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Metadata

Authors
Berger, J. O., & Pericchi, L. R.
Venue
Journal of the American Statistical Association
Publisher
Informa UK Limited
Published
1996-03
Volume
91
Issue
433
Pages
109-122
ISSN
  • 0162-1459 (ISSN-L)
  • 1537-274X
DOI
10.1080/01621459.1996.10476668
OpenAlex
W2101122849
Cited by
1,007
References
4
FWCI
23.75
Type
journal article
Language
en
Open access
closed

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Berger, J. O., & Pericchi, L. R. (1996). The Intrinsic Bayes Factor for Model Selection and Prediction. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1996.10476668

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