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Bayesian Geostatistical Design

Author

Listed:
  • Peter Diggle

    (Medical Statistics Unit, Lancaster University, UK & Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health)

  • Soren Lophaven

    (Informatics and Mathematical Modelling, Technical University of Denmark)

Abstract

This paper describes the use of model-based geostatistics for choosing the optimal set of sampling locations, collectively called the design, for a geostatistical analysis. Two types of design situations are considered. These are retrospective design, which concerns the addition of sampling locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing optimal positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model parameter values are unknown. The results show that in this situation a wide range of inter-point distances should be included in the design, and the widely used regular design is therefore not the optimal choice.

Suggested Citation

  • Peter Diggle & Soren Lophaven, 2004. "Bayesian Geostatistical Design," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1042, Berkeley Electronic Press.
    • Handle: RePEc:bep:jhubio:1042
    Note: oai:bepress.com:jhubiostat-1042
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    References listed on IDEAS

    as
    1. Le, Nhu D. & Zidek, James V., 1992. "Interpolation with uncertain spatial covariances: A Bayesian alternative to Kriging," Journal of Multivariate Analysis, Elsevier, vol. 43(2), pages 351-374, November.
    2. Hååvard Rue & Hååkon Tjelmeland, 2002. "Fitting Gaussian Markov Random Fields to Gaussian Fields," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 31-49, March.
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