Design-based Spatial Inference

Design-Based Spatial Inference is a spatial inference methodology that belongs to the class of design-based approaches in spatial statistics, distinguished by its reliance on the sampling design as the foundation for valid inference about population parameters, rather than assuming a specific stochastic model for the spatial process.

17

Publications

1.3K

Citations

49

Authors

36

Institutions

Publications per year

2017–2026

7

Authors

49

  1. SV
    Stephen V. Stehman

    SUNY College of Environmental Science and Forestry, United States

    3 publications · 244 citations

    3

    h-index

  2. AR
    Anthony R. Olsen

    Environmental Protection Agency, United States

    2 publications · 100 citations

    2

    h-index

  3. MD
    Michael Dumelle

    NOAA National Marine Fisheries Service Alaska Fisheries Science Center, United States

    2 publications · 100 citations

    2

    h-index

  4. ZC
    Zhidong Cao

    Chinese Academy of Sciences, China

    1 publications · 62 citations

    1

    h-index

  5. JD
    J.J. de Gruijter

    Wageningen University & Research, Netherlands

    1 publications · 16 citations

    1

    h-index

1–5 of 49

Venues

  1. Remote Sensing of Environment

    3 publications · 244 citations

    3

    h-index

  2. Canadian Journal of Forest Research

    2 publications · 354 citations

    2

    h-index

  3. Journal of Statistical Software

    JSS · 1 publications · 73 citations

    1

    h-index

  4. Scandinavian Journal of Statistics

    1 publications · 26 citations

    1

    h-index

  5. Methods in Ecology and Evolution

    1 publications · 27 citations

    1

    h-index

1–5