Abstract
This paper presents an extension of the Geostatistical model under preferential sampling in order to accommodate possible local repulsion effects. This local repulsion can be caused by the researcher in charge of collecting data who, after observing the stochastic process of interest in a specific location, avoids collecting new samples near this place. Proceeding in this way, the resulting sampling design would in practice include a repulsion window centered on each sampling location, even though the researcher was planning the sample preferentially. This perturbation in the Geostatistical model under preferential sampling can be modeled through a discrete nonhomogeneous stochastic process over a partition composed of M subregions of the study area, where only one sample lies in each subregion. Simulations and an application to real data are performed under the Bayesian approach and the effects of this perturbation on estimation and prediction are then discussed. The results obtained corroborate the idea that the proposed methodology corrects the distortions caused by this perturbation, thus mitigating the effects on inference and spatial prediction.
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Handling Editor: Bryan F. J. Manly
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Ferreira, G.d.S. Geostatistics under preferential sampling in the presence of local repulsion effects. Environ Ecol Stat 27, 549–570 (2020). https://doi.org/10.1007/s10651-020-00458-0
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DOI: https://doi.org/10.1007/s10651-020-00458-0