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
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Parents
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1–5 of 43
133
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107

Centre National de la Recherche Scientifique
Paris, France
4 h-index
7 publications
1.2K citations
Commissariat à l'Énergie Atomique et aux Énergies Alternatives
Paris, France
4 h-index
6 publications
556 citations
Siemens Healthcare (United States)
Malvern, United States
2 h-index
4 publications
155 citations
Paul Scherrer Institute
Villigen, Switzerland
2 h-index
8 publications
88 citations
CEA Paris-Saclay
Gif-sur-Yvette, France
2 h-index
3 publications
380 citations
University College London
London, United Kingdom
2 h-index
3 publications
213 citations
University of Alabama in Huntsville
Huntsville, United States
2 h-index
7 publications
247 citations
Geophysical Laboratory
Washington, United States
2 h-index
2 publications
22 citations
1–8 of 107
3 publications · 101 citations
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3 publications · 219 citations
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2 publications · 89 citations
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Repository · 2 publications · 58 citations
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2 publications · 30 citations
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