Unate distributions require θ̃(n^{3/2}) samples for uniformity testing and allow Õ(n^{3/2}) conditional samples for unateness testing in the subcube model.
An error bound in the Sudakov-Fernique inequality
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We obtain an asymptotically sharp error bound in the classical Sudakov-Fernique comparison inequality for finite collections of gaussian random variables. Our proof is short and self-contained, and gives an easy alternative argument for the classical inequality, extended to the case of non-centered processes.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Stochastic sensitivity analysis for matched studies finds worst-case conditional laws for hidden confounders instead of worst-case realizations, controlled by a sensitivity parameter that permits imperfect alignment with potential outcomes and yields higher robustness than conventional methods.
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Testing Unate Distributions
Unate distributions require θ̃(n^{3/2}) samples for uniformity testing and allow Õ(n^{3/2}) conditional samples for unateness testing in the subcube model.
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Stochastic Sensitivity Analysis for Matched Observational Studies
Stochastic sensitivity analysis for matched studies finds worst-case conditional laws for hidden confounders instead of worst-case realizations, controlled by a sensitivity parameter that permits imperfect alignment with potential outcomes and yields higher robustness than conventional methods.