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On the choice of the mesh for the analysis of geostatistical data using R-INLA

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  • Ana Julia Righetto
  • Christel Faes
  • Yannick Vandendijck
  • Paulo Justiniano Ribeiro

Abstract

Many methods used in spatial statistics are computationally demanding, and so, the development of more computationally efficient methods has received attention. A important development is the integrated nested Laplace approximation method which is carry out Bayesian analysis more efficiently This method, for geostatistical data, is done considering the SPDE approach that requires the creation of a mesh overlying the study area and all the obtained results depend on it. The impact of the mesh on inference and prediction is investigated through simulations. As there is no formal procedure to specify it, we investigate a guideline to create an optimal mesh.

Suggested Citation

  • Ana Julia Righetto & Christel Faes & Yannick Vandendijck & Paulo Justiniano Ribeiro, 2020. "On the choice of the mesh for the analysis of geostatistical data using R-INLA," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(1), pages 203-220, January.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:1:p:203-220
    DOI: 10.1080/03610926.2018.1536209
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    Cited by:

    1. Aaron Osgood‐Zimmerman & Jon Wakefield, 2023. "A Statistical Review of Template Model Builder: A Flexible Tool for Spatial Modelling," International Statistical Review, International Statistical Institute, vol. 91(2), pages 318-342, August.

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