2 papers
stat.ME2026
Coarse-to-fine spatial GLMM for scalable prediction and multiscale analysis
Daisuke Murakami, Alexis Comber, Takahiro Yoshida +3
We develop a coarse-to-fine generalized linear mixed model (CF-GLMM) for scalable spatial prediction of exponential-family responses. CF-GLMM extends coarse-to-fine spatial modelin…
stat.ME2025
Coarse-to-fine spatial modeling: A scalable, machine-learning-compatible spatial model
Daisuke Murakami, Alexis Comber, Takahiro Yoshida +3
This study proposes coarse-to-fine spatial modeling (CFSM) as a scalable and machine learning-compatible alternative to conventional spatial process models. Unlike conventional cov…