Model-based Sampling
Model-Based Sampling is a sampling paradigm within Sampling Theory that, as a conceptual framework, treats the population values as random variables generated according to an underlying probabilistic model, distinguishing it from design-based approaches by relying on model assumptions rather than randomization in the selection mechanism to support inference.
64
Publications
3.9K
Citations
148
Authors
108
Institutions
Publications per year
2017–2026
13
Variants
Superpopulation Approach
Parents
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1–5 of 64
148
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108
Wageningen University & Research
Wageningen, The Netherlands
3 h-index
4 publications
95 citations
University of California, Los Angeles
Los Angeles, United States
2 h-index
2 publications
88 citations
University of Michigan
Ann Arbor, United States
2 h-index
2 publications
91 citations

Australian National University
Canberra, Australia
2 h-index
3 publications
91 citations

University of Massachusetts Amherst
Amherst Center, United States
2 h-index
2 publications
88 citations

University of Alberta
Edmonton, Canada
1 h-index
1 publications
19 citations
National Agricultural Statistics Service
Washington, United States
1 h-index
1 publications
16 citations
ISRIC - World Soil Information
Wageningen, Netherlands
1 h-index
2 publications
808 citations
1–8 of 108
8 publications · 517 citations
8
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3 publications · 131 citations
3
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3 publications · 159 citations
3
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3 publications · 367 citations
3
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2 publications · 142 citations
2
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1–5