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Found 2 papers, 1 papers with code
Near-Optimal Non-Parametric Sequential Tests and Confidence Sequences with Possibly Dependent Observations
The type-I-error results primarily leverage a martingale strong invariance principle and establish that these tests (and their implied confidence sequences) have type-I error rates asymptotically equivalent to the desired (possibly varying) $\alpha$-level.
Adaptive Sequential Design for a Single Time-Series
The current work is motivated by the need for robust statistical methods for precision medicine; as such, we address the need for statistical methods that provide actionable inference for a single unit at any point in time.
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