Data Coarsening is a data abstraction technique that reduces the granularity of data representations by merging or omitting fine-grained details to produce simplified, higher-level structures while preserving essential patterns or relationships for analysis.
21
Publications
1.5K
Citations
37
Authors
21
Institutions
Publications per year
2017–2026
3
Parents
Publications
21
Daniel F. Heitjan, Donald B. Rubin · The Annals of Statistics · 1991 · 585 citations · Full text
Ignorability and Coarse Data: Some Biomedical Examples
Daniel F. Heitjan · Biometrics · 1993 · 106 citations
A new examination of classical coarsening theory
L. C. Brown · Acta Metallurgica · 1989 · 99 citations
1–5 of 21
Authors
37
- DF
5
h-index
- RDRobert D. Falgout
Lawrence Livermore National Laboratory, United States
3 publications · 228 citations
3
h-index
- AB
1
h-index
- TM
1
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- CS
1
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1–5 of 37
Institutions
21
University of Pennsylvania
Philadelphia, United States
2 h-index
2 publications
69 citations
St Thomas' Hospital
London, United Kingdom
1 h-index
1 publications
23 citations
Auburn University
Auburn, United States
1 h-index
1 publications
10 citations
King's College London
London, United Kingdom
1 h-index
1 publications
23 citations
University of British Columbia
Vancouver, Canada
1 h-index
1 publications
99 citations
Universidade Federal do Paraná
Curitiba, Brazil
1 h-index
2 publications
38 citations
Penn State Milton S. Hershey Medical Center
Hershey, United States
1 h-index
1 publications
106 citations
University of Cologne
Cologne, Germany
1 h-index
3 publications
45 citations
1–8 of 21
Venues
- Biometrics
2 publications · 149 citations
2
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- RePEc: Research Papers in Economics
Repository · 1 publications · 10 citations
1
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- Journal of the Royal Statistical Society Series B (Statistical Methodology)
1 publications · 29 citations
1
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- Political Analysis
1 publications · 42 citations
1
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- The Annals of Statistics
1 publications · 585 citations
1
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1–5