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.
and Romano, Joseph P
4 Pith papers cite this work, alongside 224 external citations. Polarity classification is still indexing.
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A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.
A Bayesian approach with SMC inference learns discrete causal representations from heterogeneous domains, demonstrated on social survey data.
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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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Optimal-transport couplings are used to construct confidence intervals that reduce coverage error relative to classical quantile-based intervals, with consistency theory and data-driven hyperparameters.
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Testing Conditional Independence via the Spectral Generalized Covariance Measure: Beyond Euclidean Data
A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.
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