Develops a multi-target evaluation framework for winner's curse corrections in experiments and proposes an adaptive empirical likelihood procedure that achieves asymptotically valid confidence intervals without resampling tuning.
Management Science , volume =
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A Bayesian predictive model adaptively selects martingale factors to construct asymptotically log-optimal confidence sequences for bounded means while preserving anytime validity under misspecification.
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Valuing Winners: When and How to Correct for Selection Bias in Randomized Experiments
Develops a multi-target evaluation framework for winner's curse corrections in experiments and proposes an adaptive empirical likelihood procedure that achieves asymptotically valid confidence intervals without resampling tuning.
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Asymptotically Log-Optimal Bayes-Assisted Confidence Sequences for Bounded Means
A Bayesian predictive model adaptively selects martingale factors to construct asymptotically log-optimal confidence sequences for bounded means while preserving anytime validity under misspecification.