Optimal homogeneous extreme predictors are non-extreme conditional quantiles of tilted distributions from the angular measure, with universally consistent peaks-over-threshold estimators.
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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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On the optimal prediction of extreme events
Optimal homogeneous extreme predictors are non-extreme conditional quantiles of tilted distributions from the angular measure, with universally consistent peaks-over-threshold estimators.
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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.