52 research outputs found
Impact of assumptions:the example of the Welch-analysis of mammography screening effectiveness
BMI at school age and incident asthma admissions in early adulthood:A prospective study of 310,211 children
Bayesian Geostatistical Design
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. Copyright 2005 Board of the Foundation of the Scandinavian Journal of Statistics..
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