mlsanityData to 7 Oct 2026

On Stopping Times of Power-one Sequential Tests: Tight Lower and Upper Bounds

arXiv 2504.19952 ↗First posted 28 Apr 2025math.ST cs.LG stat.MLcited by 5

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Abstract (arXiv)

We present two general lower bounds for stopping times of sequential tests between arbitrary composite nulls mathcal P and alternatives mathcal Q. The first lower bound is for the ``Wald setting'' where the type-1 error level α approaches zero for a fixed alternative Q ∈ mathcal Q, and equals log(1/α) divided by a certain infimum KL divergence between mathcal P and Q, termed operatornameKLinf. The second lower bound applies to the ``Farrell setting'', where α is fixed and operatornameKLinf approaches 0 along a sequence of alternatives such that the required expected sample size along that sequence is of order at least operatornameKL-1inf log log operatornameKL-1inf. Our main contribution is the generality of these bounds, which hold in non-parametric, composite settings, without requiring a dominating reference measure, substantially generalizing the known parametric results. We also provide sufficient conditions for matching upper bounds and show that these are met in several nontrivial non-parametric cases.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 38 references that are in the map. First posted on arXiv 28 Apr 2025.

Builds on

13 of 38 references.

References · 13

  1. 2014On the Complexity of Best Arm Identification in Multi-Armed Bandit Modelscited by 280
  2. 2018Time-uniform, nonparametric, nonasymptotic confidence sequencescited by 220
  3. 2016Explore First, Exploit Next: The True Shape of Regret in Bandit Problemscited by 78
  4. 2024Hypothesis testing with e-valuescited by 70
  5. 2021Tight Concentrations and Confidence Sequences from the Regret of Universal Portfoliocited by 42
  6. 2021Nonparametric Two-Sample Testing by Bettingcited by 36
  7. 2021Testing exchangeability: fork-convexity, supermartingales, and e-processescited by 29
  8. 2023Auditing Fairness by Bettingcited by 24
  9. 2015On the Optimal Sample Complexity for Best Arm Identificationcited by 17
  10. 2020Optimal Best-Arm Identification Methods for Tail-Risk Measurescited by 17
  11. 2024The numeraire e-variable and reverse information projectioncited by 11
  12. 2025Instance-Log-Optimality of Portfolio-Based E-Processes and their Sequential Hypothesis Testscited by 10
1 more
  1. 2025Optimistic Interior Point Methods for Sequential Hypothesis Testing by Bettingcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.