Log Gaussian Cox processes and spatially aggregated disease incidence data.

@article{Li2012LogGC,
  title={Log Gaussian Cox processes and spatially aggregated disease incidence data.},
  author={Ye Li and Patrick Brown and Dionne Gesink and H{\aa}vard Rue},
  journal={Statistical methods in medical research},
  year={2012},
  volume={21 5},
  pages={479-507}
}
This article presents a methodology for modeling aggregated disease incidence data with the spatially continuous log-Gaussian Cox process. Statistical models for spatially aggregated disease incidence data usually assign the same relative risk to all individuals in the same reporting region (census areas or postal regions). A further assumption that the relative risks in two regions are independent given their neighbor's risks (the Markov assumption) makes the commonly used Besag-York-Molli… CONTINUE READING
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