Count Data Small Area Estimation

Count Data Small Area Estimation is a statistical methodology within small area estimation that specifically addresses the problem of producing reliable estimates for small domains or subpopulations when the variable of interest consists of counts, typically modeled using discrete distributions such as the Poisson or negative binomial, and incorporates auxiliary information and hierarchical modeling techniques to improve estimation precision in data-sparse areas.

9

Publications

297

Citations

29

Authors

21

Institutions

Publications per year

2017–2026

2

Authors

29

  1. NS
    Nicola Salvati

    University of Pisa, Italy

    2 publications · 83 citations

    2

    h-index

  2. RC
    Ray Chambers

    University of Wollongong, Australia

    2 publications · 83 citations

    2

    h-index

  3. NV
    Nathalie Vandal

    Institut National de Santé Publique du Québec, Canada

    1 publications · 29 citations

    1

    h-index

  4. JJ
    Jiming Jiang

    University of California, Davis, United States

    1 publications · 15 citations

    1

    h-index

  5. RM
    Richard Mitchell

    University of Glasgow, United Kingdom

    1 publications · 14 citations

    1

    h-index

1–5 of 29

Venues

  1. Environment and Planning A Economy and Space

    1 publications · 14 citations

    1

    h-index

  2. Health & Place

    1 publications · 26 citations

    1

    h-index

  3. Journal of Survey Statistics and Methodology

    1 publications · 15 citations

    1

    h-index

  4. Canadian Journal of Statistics

    CJS · 1 publications · 11 citations

    1

    h-index

  5. Scandinavian Journal of Statistics

    1 publications · 48 citations

    1

    h-index

1–5