A framework for optimal posterior e-values with non-convex composite hypotheses, demonstrated via statistical tests for multiple voting systems including the first treatment of Schulze.
Growth-optimal e-variables and an extension to the multivariate Csiszar-Sanov-Chernoff theorem
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The GROW value for bounded e-variables equals the minimal relative entropy between weak-* closed convex hulls of arbitrary composite null and alternative sets.
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Optimal Posterior E-values with Non-Convex Parameter Sets with Applications to Voting Systems
A framework for optimal posterior e-values with non-convex composite hypotheses, demonstrated via statistical tests for multiple voting systems including the first treatment of Schulze.
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Strong duality for the GROW criterion
The GROW value for bounded e-variables equals the minimal relative entropy between weak-* closed convex hulls of arbitrary composite null and alternative sets.