Bentkus-type asymptotic e-values eliminate the missing factor and deliver sharper inference than prior asymptotic e-values in post-hoc and multiple testing settings.
arXiv preprint arXiv:2509.02517 , year=
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Closed BH improves the Benjamini-Hochberg procedure via e-Closure, controlling FDR under PRDS or weaker assumptions while never rejecting fewer hypotheses.
Post-hoc conformal selection creates a path of selection sets with estimated false discovery proportions, enabling data-driven adaptive FDR control with average reliability guarantees via e-variables and e-BH.
Demonstrates formal equivalence between adaptive design tools and e-value sequential tests while noting differences in emphasis on flexibility aspects.
Domino guarantees k-bFDR control under arbitrary dependence via the closure principle, extending boundary FDR methods to general settings for both p-values and e-values.
Active hypothesis testing framework uses auxiliary statistics for data-adaptive budget allocation to produce valid p-values or e-values with optimality under independence and admissibility under dependence.
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Bentkus-type asymptotic e-values
Bentkus-type asymptotic e-values eliminate the missing factor and deliver sharper inference than prior asymptotic e-values in post-hoc and multiple testing settings.
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A Uniform Improvement of the Benjamini-Hochberg Procedure via e-Closure
Closed BH improves the Benjamini-Hochberg procedure via e-Closure, controlling FDR under PRDS or weaker assumptions while never rejecting fewer hypotheses.
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Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables
Post-hoc conformal selection creates a path of selection sets with estimated false discovery proportions, enabling data-driven adaptive FDR control with average reliability guarantees via e-variables and e-BH.
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Anytime-valid testing with e-values and confirmatory adaptive designs
Demonstrates formal equivalence between adaptive design tools and e-value sequential tests while noting differences in emphasis on flexibility aspects.
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Generalized Boundary FDR Control under Arbitrary Dependence: An Approach on Closure Principle
Domino guarantees k-bFDR control under arbitrary dependence via the closure principle, extending boundary FDR methods to general settings for both p-values and e-values.
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Active Hypothesis Testing under Computational Budgets with Applications to GWAS and LLM
Active hypothesis testing framework uses auxiliary statistics for data-adaptive budget allocation to produce valid p-values or e-values with optimality under independence and admissibility under dependence.