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Prior‐free Bayes Factors Based on Data Splitting
Author(s) -
Hart Jeffrey D.,
Malloure Matthew
Publication year - 2019
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12323
Subject(s) - bayes' theorem , bayes factor , consistency (knowledge bases) , parametric statistics , statistics , mathematics , bayes' rule , maximum likelihood , exponential family , computer science , econometrics , bayesian probability , artificial intelligence
Summary Bayes factors that do not require prior distributions are proposed for testing one parametric model versus another. These Bayes factors are relatively simple to compute, relying only on maximum likelihood estimates, and are Bayes consistent at an exponential rate for nested models even when the smaller model is true . These desirable properties derive from the use of data splitting. Large sample properties, including consistency, of the Bayes factors are derived, and a simulation study explores practical concerns. The methodology is illustrated with civil engineering data involving compressive strength of concrete.

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