Bayesian geostatistical design

Peter Diggle, Søren Lophaven

Research output: Contribution to journalArticle

Abstract

This paper describes the use of model-based geostatistics for choosing the set of sampling locations, collectively called the design, to be used in a geostatistical analysis. Two types of design situation 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 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 often not the best choice.

Original languageEnglish (US)
Pages (from-to)53-64
Number of pages12
JournalScandinavian Journal of Statistics
Volume33
Issue number1
DOIs
StatePublished - Mar 2006
Externally publishedYes

Keywords

  • Bayesian inference
  • Model-based geostatistics
  • Spatial design

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

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