Bayesian Geostatistical Design

Peter Diggle and Soren Lophaven
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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

No 1042, Johns Hopkins University Dept. of Biostatistics Working Paper Series from Berkeley Electronic Press

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.

Keywords: Model-based geostatistics; Bayesian inference; Spatial design (search for similar items in EconPapers)
Date: 2004-07-11
Note: oai:bepress.com:jhubiostat-1042
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