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.
URL http://link.springer.com/10.1007/ 978-0-387-21736-9
5 Pith papers cite this work, alongside 1,044 external citations. Polarity classification is still indexing.
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In a 1D test case, backward diffusion paths converge to the infinite-pattern limit on a 1/sqrt(p) scale with infinite mean square deviation, enabling an extrapolation algorithm for density estimation and generalization.
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
Assumed density filtering and smoothing for neural network surrogate models is enabled by analytic computation of output moments, yielding more accurate state estimates and improved LQR performance on stochastic Lorenz and Wiener systems.
A multimodal registration pipeline models splints as rigid mandible transformations to quantify TMJ configuration changes via error propagation and surface metrics.
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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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Path convergence in diffusion models
In a 1D test case, backward diffusion paths converge to the infinite-pattern limit on a 1/sqrt(p) scale with infinite mean square deviation, enabling an extrapolation algorithm for density estimation and generalization.
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A Semi-Supervised Kernel Two-Sample Test
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
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Assumed Density Filtering and Smoothing with Neural Network Surrogate Models
Assumed density filtering and smoothing for neural network surrogate models is enabled by analytic computation of output moments, yielding more accurate state estimates and improved LQR performance on stochastic Lorenz and Wiener systems.
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