4 citations · 4 across the 1 of their papers we have counts for
4 papers · 1 filter
A memory-free spatial additive mixed modeling for big spatial data
Daisuke Murakami, Daniel A. Griffith
This study develops a spatial additive mixed modeling (AMM) approach estimating spatial and non-spatial effects from large samples, such as millions of observations. Although fast…
Low rank spatial econometric models
Daisuke Murakami, Hajime Seya, Daniel A. Griffith
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…
Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions
Daisuke Murakami, Daniel A. Griffith
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 i…
The importance of scale in spatially varying coefficient modeling
Daisuke Murakami, Binbin Lu, Paul Harris +4
While spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study…