Hierarchical Models for Spatial Data with Errors that are Correlated with the Latent Process

@article{Bradley2019HierarchicalMF,
  title={Hierarchical Models for Spatial Data with Errors that are Correlated with the Latent Process},
  author={Jonathan R. Bradley and Christopher K. Wikle and Scott H. Holan},
  journal={Statistica Sinica},
  year={2019}
}
Prediction of a spatial process using a “big dataset” has become a topical area of research over the last decade. The available solutions often involve placing strong assumptions on the error process associated with the data. Specifically, it has typically been assumed that the data are equal to the spatial process of principal interest plus a mutually independent error process. This is done to avoid modeling confounded cross-covariances between the signal and noise within an additive model. In… 

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