Sequential Equivalence Testing

Sequential Equivalence Testing is a statistical hypothesis testing framework that extends equivalence testing to sequential experimental designs by enabling interim analyses and early stopping while controlling Type I error rates, thereby distinguishing itself from fixed-sample equivalence tests through its adaptive, data-driven sampling process.

22

Publications

699

Citations

54

Authors

34

Institutions

Publications per year

2017–2026

2

Authors

54

  1. DS
    David Siegmund

    Stanford University, United States

    2 publications · 52 citations

    2

    h-index

  2. KC
    Kuang-Chien Chen

    University of California, Santa Barbara, United States

    2 publications · 55 citations

    2

    h-index

  3. HR
    Herbert Robbins

    Columbia University, United States

    2 publications · 52 citations

    2

    h-index

  4. SH
    Shi‐Yu Huang

    National Tsing Hua University, Taiwan

    2 publications · 55 citations

    2

    h-index

  5. KC
    Kwang‐Ting Cheng

    University of California, Santa Barbara, United States

    2 publications · 55 citations

    2

    h-index

1–5 of 54

Venues

  1. Statistics in Medicine

    2 publications · 37 citations

    2

    h-index

  2. 2

    h-index

  3. Biometrics

    2 publications · 151 citations

    2

    h-index

  4. Design Automation for Embedded Systems

    1 publications · 13 citations

    1

    h-index

  5. IEEE Design & Test of Computers

    1 publications · 29 citations

    1

    h-index

1–5