# Spatial regression modeling using the spmoran package: Boston housing price data examples

@inproceedings{Murakami2017SpatialRM, title={Spatial regression modeling using the spmoran package: Boston housing price data examples}, author={Daisuke Murakami}, year={2017} }

An approximate Gaussian process (GP or kriging model), which is interpretable in terms of the Moran coefficient (MC), is used for modeling the spatial process. The approximate GP is defined by a linear combination of the Moran eigenvectors (MEs) corresponding to positive eigenvalue, which are known to explain positive spatial dependence. The resulting spatial process describes positively dependent map patterns (i.e., MC > 0), which are dominant in regional science (Griffith, 2003). Below, the… Expand

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Compositionally-warped additive mixed modeling for a wide variety of non-Gaussian spatial data

- Computer Science, Mathematics
- 2021

A general framework for fast and flexible non-Gaussian regression, especially for spatial/spatiotemporal modeling is developed and the developed model, termed the compositionally-warped additive mixed model (CAMM), provides intuitively reasonable coefficient estimates and outperforms AMM in terms of prediction accuracy. Expand

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