Error Model Parameterization is a statistical modeling framework that specifies the structure and parameters of the error term in a quantitative model to account for residual unexplained variability, distinguishing itself by formalizing how measurement noise or model inadequacy is represented and estimated.
53
Publications
5.1K
Citations
176
Authors
90
Institutions
Publications per year
2017–2026
15
Publications
53
ART: a next-generation sequencing read simulator
Weichun Huang, Leping Li, Jason R. Myers et al. · Bioinformatics · 2011 · 1.8K citations
Unifying error structures in commonly used biotracer mixing models
Brian C. Stock, Brice X. Semmens · Ecology · 2016 · 355 citations · Full text
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Authors
176
- WZ
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- GH
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1–5 of 176
Institutions
90
University of California San Diego
San Diego, United States
2 h-index
7 publications
825 citations
Western University
London, Canada
2 h-index
3 publications
155 citations
Forschungszentrum Jülich
Jülich, Germany
2 h-index
2 publications
107 citations
Universitat Politècnica de València
Valencia, Spain
2 h-index
3 publications
63 citations
Virginia Tech
Blacksburg, United States
2 h-index
9 publications
1.1K citations
The University of Texas at Austin
Austin, United States
2 h-index
12 publications
972 citations
University of Bonn
Bonn, Germany
2 h-index
3 publications
232 citations
Royal Netherlands Meteorological Institute
De Bilt, The Netherlands
1 h-index
3 publications
42 citations
1–8 of 90
Venues
- International Journal of Pressure Vessels and Piping
2 publications · 119 citations
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- Water Resources Research
2 publications · 34 citations
2
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- Bioinformatics
2 publications · 1.9K citations
2
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- Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE
2 publications · 28 citations
2
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- Conicet
Repository · 1 publications · 12 citations
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