Prior‐free Bayes Factors Based on Data Splitting

Jeffrey D. Hart and Matthew Malloure

International Statistical Review, 2019, vol. 87, issue 2, 419-442

Abstract: 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.

Date: 2019
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https://doi.org/10.1111/insr.12323

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