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

Authors

148

  1. DB
    D.J. Brus

    Wageningen University & Research, Netherlands

    3 publications · 453 citations

    3

    h-index

  2. KJ
    Krista J. Gile

    University of Massachusetts Amherst, United States

    2 publications · 88 citations

    2

    h-index

  3. RJ
    Roderick J. A. Little

    University of Michigan, United States

    2 publications · 273 citations

    2

    h-index

  4. RL
    Roger L. Wright

    University of Michigan, United States

    2 publications · 91 citations

    2

    h-index

  5. JD
    J.J. de Gruijter

    Wageningen University & Research, Netherlands

    2 publications · 420 citations

    2

    h-index

1–5 of 148

Venues

  1. 8

    h-index

  2. Biometrika

    3 publications · 131 citations

    3

    h-index

  3. Statistical Science

    3 publications · 159 citations

    3

    h-index

  4. Canadian Journal of Forest Research

    3 publications · 367 citations

    3

    h-index

  5. Technometrics

    2 publications · 142 citations

    2

    h-index

1–5