We derive powerful e-processes for sequential stochastic-dominance testing, with power-one guarantees for first- and higher-order dominance.
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5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5representative citing papers
Adapts bandit algorithms to the Cox PH survival model for online treatment optimization under censoring, with theoretical sublinear regret and validation on simulations plus SEER cancer data.
Sequential testing with tailored stopping rules lets model evaluation halt early once statistical needs (CI width, significance, equivalence) are met, saving up to 80% compute on VLM leaderboards.
The paper derives an anytime-valid global test for dose-ranging trials that subtracts a predictable 'selection charge' from the running maximum dose effect to correct winner's curse bias while controlling Type I error.
Proposes an e-process-based sequential diagnostic that detects misspecified PDE inverse problem fits earlier than standard discrepancy methods while providing anytime-valid type-I error control.
citing papers explorer
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Betting on Bets: Anytime-Valid Tests for Stochastic Dominance
We derive powerful e-processes for sequential stochastic-dominance testing, with power-one guarantees for first- and higher-order dominance.
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Online Survival Analysis: A Bandit Approach under Cox PH Model
Adapts bandit algorithms to the Cox PH survival model for online treatment optimization under censoring, with theoretical sublinear regret and validation on simulations plus SEER cancer data.
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Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data
Sequential testing with tailored stopping rules lets model evaluation halt early once statistical needs (CI width, significance, equivalence) are met, saving up to 80% compute on VLM leaderboards.
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Mitigating the Winner's Curse While Controlling Multiplicity: e-Process Methods for Anytime-Valid Inference in Dose-Ranging Trials
The paper derives an anytime-valid global test for dose-ranging trials that subtracts a predictable 'selection charge' from the running maximum dose effect to correct winner's curse bias while controlling Type I error.
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Sequential Structure-Sensitive Residual Diagnostics for PDE Inverse Problems
Proposes an e-process-based sequential diagnostic that detects misspecified PDE inverse problem fits earlier than standard discrepancy methods while providing anytime-valid type-I error control.