HIGHLIGHTS
SUMMARY
In, they consider p-values in linear models and propose new monotonic minimum Bayes factors that depend on the sample size and converge to -e · p · log( p) as the sample size approaches infinity, which implies it is not consistent, as Bayes factors are. The authors propose to adjust the Robust Lower Bound -e · p · log( p) so that it behaves in a similar or approximate way to actual Bayes factors for any sample size. The effect of adjusting this minimum Bayes factor with the sample size is shown in a simulation in Section . . .
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