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A spatial model for rare binary events
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Journal Article
A spatial model for rare binary events
2017
Overview
Many predominant spatial methods for binary data use a latent Gaussian process to capture spatial dependence. However, this may not be appropriate for rare data because these methods based on Gaussian processes are asymptotically independent as the event probability goes to zero. In this paper, we propose a method for rare binary data that builds on spatial extreme value theory. We model binary events as exceedances of a max-stable process and show that this construction maintains spatial dependence even as the event probability goes to zero. We compare our model to spatial probit and logistic methods through a simulation study and analysis of a survey of
Tamarix ramosissima
and
Hedysarum scoparium
. We find some evidence that for very rare data the max-stable extension provides an improvement in spatial prediction compared to Gaussian models.
Publisher
Springer US,Springer Nature B.V
