Sparse Sampling Model Development
Sparse Sampling Model Development is a model development methodology in which the construction and training of predictive or analytical models are based on intentionally limited and non-dense data samples, distinguishing it from conventional approaches by prioritizing efficiency and representativeness under sparse data acquisition conditions.
17
Publications
644
Citations
66
Authors
30
Institutions
Publications per year
2017–2026
2
Parents
17
Sparse sampling: Spatial design for monitoring stream networks
Statistics Surveys · 2008 · 57 citations · Full text
1–5 of 17
66
University at Buffalo, State University of New York, United States
2 publications · 37 citations
2
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1
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1
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1
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1
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1–5 of 66
30
University at Buffalo, State University of New York
Buffalo, United States
2 h-index
4 publications
77 citations

Leicester Royal Infirmary
Leicester, United Kingdom
1 h-index
2 publications
76 citations

University of Leicester
Leicester, United Kingdom
1 h-index
2 publications
76 citations
Bayer (Germany)
Leverkusen, Germany
1 h-index
2 publications
76 citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
1 h-index
2 publications
34 citations

Western University
London, Canada
1 h-index
8 publications
256 citations

University of Maryland, Baltimore
Baltimore, United States
1 h-index
2 publications
34 citations

Swedish University of Agricultural Sciences
Uppsala, Sweden
1 h-index
2 publications
28 citations
1–8 of 30
JMR · 2 publications · 82 citations
2
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BJCP · 2 publications · 162 citations
2
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1 publications · 57 citations
1
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1 publications · 47 citations
1
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1 publications · 14 citations
1
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1–5