4 citations · 10 across the 8 of their papers we have counts for
5 papers · 1 filter
Scalable coarse-to-fine spatial downscaling
Daisuke Murakami, Yongwan Chun, Takahiro Yoshida +1
This study proposes coarse-to-fine downscaling (CF-DS), a scalable spatial downscaling method extending coarse-to-fine spatial modeling. Unlike conventional spatial-statistical dow…
Sub-model aggregation for scalable eigenvector spatial filtering: Application to spatially varying coefficient modeling
Daisuke Murakami, Shonosuke Sugasawa, Hajime Seya +1
This study proposes a method for aggregating/synthesizing global and local sub-models for fast and flexible spatial regression modeling. Eigenvector spatial filtering (ESF) was use…
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 filtered unconditional quantile regression: Application to a hedonic analysis
Daisuke Murakami, Hajime Seya
Unconditional quantile regression (UQR) attracts attention in various fields to investigate the impacts of explanatory variables on quantiles of the marginal distribution of an exp…
A Moran coefficient-based mixed effects approach to investigate spatially varying relationships
Daisuke Murakami, Takahiro Yoshida, Hajime Seya +2
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…