Reformulation invariance on inference problems forces minimization of the Kullback-Leibler divergence, narrowing from f-divergences to alpha-divergences to KL.
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Derives exact operating characteristic corrections and a numerical search over sample sizes to obtain optimal two-stage Bayes factor designs for two-arm binary-endpoint phase II trials that minimize expected sample size under the null.
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Reformulation Invariance and the Axiomatic Foundations of Inference
Reformulation invariance on inference problems forces minimization of the Kullback-Leibler divergence, narrowing from f-divergences to alpha-divergences to KL.
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Optimal sequential two-stage Bayes Factor Design for two-arm clinical Phase II Trials with binary Endpoints
Derives exact operating characteristic corrections and a numerical search over sample sizes to obtain optimal two-stage Bayes factor designs for two-arm binary-endpoint phase II trials that minimize expected sample size under the null.