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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.ST 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Inferential models predict unobserved auxiliary values via calibrated predictive random sets before transferring plausibility to parameters, yielding valid uncertainty statements that relate fiducial, confidence, and belief-function approaches.
citing papers explorer
-
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.
-
Inferential Models: The Power of Auxiliary Variables for Reasoning with Scientific Uncertainty
Inferential models predict unobserved auxiliary values via calibrated predictive random sets before transferring plausibility to parameters, yielding valid uncertainty statements that relate fiducial, confidence, and belief-function approaches.