Bayesian Spatial Variable Selection

Bayesian Spatial Variable Selection is a statistical modeling framework that identifies relevant predictors in spatially referenced data by incorporating Bayesian inference to simultaneously account for spatial dependence and uncertainty in variable inclusion through posterior probabilities.

41

Publications

1.9K

Citations

141

Authors

90

Institutions

Publications per year

2017–2026

9

Authors

141

  1. PV
    Penelope Vounatsou

    Swiss Tropical and Public Health Institute, Switzerland

    9 publications · 530 citations

    9

    h-index

  2. JU
    Jürg Utzinger

    Swiss Tropical and Public Health Institute, Switzerland

    5 publications · 313 citations

    5

    h-index

  3. AS
    Ali Sié

    Centre de Recherche en Santé de Nouna, Burkina Faso

    3 publications · 137 citations

    3

    h-index

  4. LF
    Ludwig Fahrmeir

    Ludwig-Maximilians-Universität München, Germany

    3 publications · 210 citations

    3

    h-index

  5. AL
    Andrew Lawson

    Medical University of South Carolina, United States

    3 publications · 56 citations

    3

    h-index

1–5 of 141

Venues

  1. Journal of Computational and Graphical Statistics

    JCGS · 3 publications · 206 citations

    3

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  2. Parasites & Vectors

    3 publications · 108 citations

    3

    h-index

  3. Biometrics

    2 publications · 50 citations

    2

    h-index

  4. 2

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  5. NeuroImage

    2 publications · 107 citations

    2

    h-index

1–5