Corpus ID: 231581239

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

References

SHOWING 1-10 OF 17 REFERENCES
Spatial Autocorrelation and Spatial Filtering: Gaining Understanding Through Theory and Scientific Visualization
1 Introduction.- 1.1 Scientific Visualization.- 1.2 What Is Spatial Autocorrelation?.- 1.3 Selected Visualization Tools: An Overview.- 1.3.1 Graphical Portrayals of Spatial Autocorrelation.- 1.4 TheExpand
Eliminating N from spatial regressions
  • Spatial Statistics,
  • 2019
A memory-free spatial additive mixed modeling for big spatial data
TLDR
This study develops a spatial additive mixed modeling approach estimating spatial and non-spatial effects from large samples, such as millions of observations, with a Moran coefficient-based approach and applies it to an income analysis using United States (US) data in 2015. Expand
Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions
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 isExpand
Eigenvector Spatial Filtering for Large Data Sets: Fixed and Random Effects Approaches
TLDR
This study develops fast ESF and random effects ESF (RE-ESF), which are capable of handling very large samples, and suggests that the proposed approaches effectively remove positive spatial dependence in the residuals with very small approximation errors when the number of eigenvectors considered is 200 or more. Expand
Compositionally-Warped Gaussian Processes
TLDR
The compositionally-warped GP (CWGP) is proposed, a non-Gaussian generative model whose expressiveness follows from its deep compositional architecture, and its computational efficiency is guaranteed by the analytical inverse warping. Expand
Low rank spatial econometric models
This article presents a re-structuring of spatial econometric models in a linear mixed model framework. To that end, it proposes low rank spatial econometric models that are robust to the existenceExpand
Spatially filtered unconditional quantile regression
A Moran coefficient-based mixed effects approach to investigate spatially varying relationships
This study develops a spatially varying coefficient model by extending the random effects eigenvector spatial filtering model. The developed model has the following properties: its coefficients areExpand
Random effects specifications in eigenvector spatial filtering: a simulation study
TLDR
The main findings of this simulation are that in many cases, parameter estimates of the extended RE-ESF are more accurate than other ESF models; the elimination of the spatial component confounding with explanatory variables results in biased parameter estimates; efficiency of an accuracy maximization-based conventional ESF is comparable to RE- ESF inMany cases. Expand
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