Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions

@article{Murakami2019SpatiallyVC,
  title={Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions},
  author={Daisuke Murakami and Daniel A. Griffith},
  journal={Spatial Statistics},
  year={2019}
}
Abstract While spatially varying coefficient (SVC) modeling is popular in applied science, its computational burden is substantial. This is especially true if a multiscale property of SVC is considered. Given this background, this study develops a Moran’s eigenvector-based spatially varying coefficients (M-SVC) modeling approach that estimates multiscale SVCs computationally efficiently. This estimation is accelerated through a (i) rank reduction, (ii) pre-compression, and (iii) sequential… 
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