Typicality-based Hypothesis Testing

Typicality-Based Hypothesis Testing is a statistical inference framework that belongs to the class of hypothesis testing methods, distinguished by its reliance on the typicality of observed sequences—measured through their empirical distributions and divergence from expected probabilistic models—to determine the validity of hypotheses in information-theoretic and large-sample settings.

12

Publications

978

Citations

27

Authors

13

Institutions

Publications per year

2017–2026

2

Authors

27

  1. VV
    Venugopal V. Veeravalli

    University of Illinois Urbana-Champaign, United States

    2 publications · 42 citations

    2

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  2. SN
    Sirin Nitinawarat

    University of Illinois Urbana-Champaign, United States

    2 publications · 42 citations

    2

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  3. JA
    J. A. Hartigan

    Yale University, United States

    2 publications · 157 citations

    2

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  4. ER
    Eleanor Rosch

    University of California, Berkeley, United States

    2 publications · 560 citations

    2

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  5. CS
    Carol Simpson

    Johns Hopkins University, United States

    1 publications · 75 citations

    1

    h-index

1–5 of 27

Venues

  1. 2

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  2. 2

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  3. Sequential Analysis

    1 publications · 26 citations

    1

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  4. The American Statistician

    1 publications · 25 citations

    1

    h-index

  5. 1

    h-index

1–5