Low-count Statistics

Low-Count Statistics is a statistical framework that specifically addresses the analysis and interpretation of data sets characterized by small event counts, where traditional asymptotic approximations are invalid and exact probabilistic models, such as Poisson or binomial distributions, are required to accurately quantify uncertainty and variability.

43

Publications

2.8K

Citations

133

Authors

107

Institutions

Publications per year

2017–2026

8

Authors

133

  1. KR
    K. R. Briggs

    Paul Scherrer Institute, Switzerland

    3 publications · 32 citations

    3

    h-index

  2. MS
    M. Sibomana

    UCLouvain, Belgium

    3 publications · 123 citations

    3

    h-index

  3. KA
    K. Arzner

    Paul Scherrer Institute, Switzerland

    3 publications · 32 citations

    3

    h-index

  4. MS
    Manfred Schmidt

    National Center for Tumor Diseases, Germany

    3 publications · 32 citations

    3

    h-index

  5. AT
    A. Telleschi

    Paul Scherrer Institute, Switzerland

    3 publications · 32 citations

    3

    h-index

1–5 of 133

Venues

  1. IEEE Transactions on Medical Imaging

    3 publications · 101 citations

    3

    h-index

  2. Physics in Medicine and Biology

    3 publications · 219 citations

    3

    h-index

  3. IEEE Transactions on Nuclear Science

    2 publications · 89 citations

    2

    h-index

  4. arXiv (Cornell University)

    Repository · 2 publications · 58 citations

    2

    h-index

  5. Journal of Forecasting

    2 publications · 30 citations

    2

    h-index

1–5