Anytime-valid Inference

Anytime-Valid Inforce Inference is a statistical inference framework that, unlike conventional methods requiring fixed sample sizes or predetermined stopping rules, enables valid hypothesis testing and confidence interval construction at any data-dependent stopping time, thereby maintaining type-I error control and coverage guarantees under optional continuation or early stopping.

3

Publications

135

Citations

7

Authors

6

Institutions

Publications per year

2017–2026

3

Authors

7

  1. AR
    Aaditya Ramdas

    Carnegie Mellon University, United States

    3 publications · 135 citations

    3

    h-index

  2. ML
    Martin Larsson

    ETH Zurich, Switzerland

    2 publications · 50 citations

    2

    h-index

  3. JR
    Johannes Ruf

    University of Oxford, United Kingdom

    2 publications · 50 citations

    2

    h-index

  4. WM
    Wouter M. Koolen

    Centrum Wiskunde & Informatica, Netherlands

    2 publications · 50 citations

    2

    h-index

  5. PG
    Peter Grünwald

    Centrum Wiskunde & Informatica, Netherlands

    1 publications · 85 citations

    1

    h-index

1–5 of 7

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 50 citations

    2

    h-index

  2. Statistical Science

    1 publications · 85 citations

    1

    h-index

1–2